auto-diff research tool

1.auto-diff-storm.md

To address your query, I'll first analyze the similarities and differences between the LLM-AutoDiff framework (as described in the provided document) and DSPy, focusing on their approaches to automatic prompt engineering and optimization for large language models (LLMs). Then, I'll outline how you could adapt LLM-AutoDiff to develop a tool similar to STORM, which is a knowledge curation system that leverages LLMs to generate Wikipedia-like articles.

Similarities Between LLM-AutoDiff and DSPy

1. Automation of Prompt Engineering:

   • LLM-AutoDiff: Treats prompts as trainable parameters and uses a "backward engine" LLM to generate textual feedback (akin to gradients) for iterative refinement. This automates the tuning of prompts across multi-step workflows, reducing manual effort.
   • DSPy: Abstracts prompt engineering by allowing users to define input/output signatures and modules, which are then optimized automatically using teleprompters (optimizers). It shifts the focus from hand-crafted prompts to programmatic optimization.

   Similarity: Both frameworks aim to eliminate the trial-and-error of manual prompt engineering by introducing systematic, automated optimization processes.

2. Iterative Optimization:

   • LLM-AutoDiff: Employs an iterative refinement loop where prompts are adjusted based on feedback from a frozen LLM, prioritizing error-prone samples to enhance efficiency.
   • DSPy: Uses optimizers (e.g., BootstrapFewShotWithRandomSearch) to iteratively refine prompts or generate examples, improving performance based on a defined metric.

   Similarity: Both rely on iterative processes to enhance prompt quality, drawing inspiration from optimization techniques in machine learning.

3. Handling Complex Workflows:

   • LLM-AutoDiff: Excels in multi-step pipelines (e.g., multi-hop question answering, agent-driven tasks) by isolating sub-prompts and managing cyclic dependencies.
   • DSPy: Supports compositional pipelines through modules (e.g., ChainOfThought, Retrieval-Augmented Generation) that can be chained for complex tasks.

   Similarity: Both are designed to manage intricate LLM workflows beyond single-step tasks, making them suitable for applications like autonomous agents or knowledge-intensive systems.

4. ** Inspiration from Neural Network Training**:

   • LLM-AutoDiff: Draws on automatic differentiation concepts (e.g., backpropagation) to optimize textual prompts, akin to tuning neural network weights.
   • DSPy: Mirrors machine learning frameworks like PyTorch by treating LLM calls as modules within a pipeline, optimized via compilation.

   Similarity: Both borrow heavily from neural network optimization paradigms, adapting them to the textual domain of LLMs.

Differences Between LLM-AutoDiff and DSPy

1. Optimization Mechanism:

   • LLM-AutoDiff: Uses a "backward engine" LLM to compute textual gradients, directly refining prompts through a feedback loop. This is a continuous, gradient-like process tailored to textual inputs.
   • DSPy: Relies on discrete optimization strategies (e.g., few-shot example generation, random search) rather than gradient-based methods, compiling prompts based on predefined metrics.

   Difference: LLM-AutoDiff’s gradient-inspired approach is more analogous to continuous optimization, while DSPy’s methods are heuristic and discrete.

2. Granularity of Control:

   • LLM-AutoDiff: Offers fine-grained control by isolating sub-prompts (e.g., instructions, formats) and optimizing them independently, preventing context dilution.
   • DSPy: Abstracts prompts into higher-level signatures and modules, which may limit fine-grained tweaking unless explicitly designed into the pipeline.

   Difference: LLM-AutoDiff provides more granular prompt manipulation, while DSPy emphasizes modularity and abstraction.

3. Dependency Handling:

   • LLM-AutoDiff: Explicitly addresses cyclic dependencies in workflows, making it robust for iterative or recursive LLM calls.
   • DSPy: While capable of handling sequential tasks, it does not explicitly focus on cyclic dependencies, relying instead on pipeline composition.

   Difference: LLM-AutoDiff is better suited for tasks with recursive or looped structures, whereas DSPy excels in linear or modular compositions.

4. Implementation Philosophy:

   • LLM-AutoDiff: Positions itself as a low-level, flexible framework, akin to a differentiable programming library for LLMs.
   • DSPy: Acts as a higher-level framework, providing pre-built modules and optimizers for ease of use and rapid development.

   Difference: LLM-AutoDiff is more foundational and customizable, while DSPy is more structured and user-friendly.

Developing a STORM-like Tool with LLM-AutoDiff

STORM (developed with DSPy) is a system that generates Wikipedia-like articles by leveraging LLMs for knowledge curation. It involves:

• Perspective-Guided Question Asking: Generating questions from multiple perspectives to gather comprehensive information.
• Retrieval-Augmented Generation (RAG): Using external sources to answer questions and build content.
• Iterative Refinement: Refining the article through multiple LLM calls, synthesizing information into a coherent output.

Here’s how you could adapt LLM-AutoDiff to create a similar tool:

Step 1: Define the Workflow

STORM’s workflow involves iterative question generation, retrieval, and synthesis. With LLM-AutoDiff:

• Break the process into sub-components:
  • Question Generation Prompt: Generates diverse, perspective-based questions.
  • Retrieval Prompt: Queries external sources (e.g., web search) to gather answers.
  • Synthesis Prompt: Combines retrieved data into a cohesive article.
• Treat each sub-component’s prompt as a trainable parameter, optimized independently using LLM-AutoDiff.

Step 2: Implement Cyclic Optimization

STORM refines its output iteratively. LLM-AutoDiff’s strength in handling cyclic dependencies can be leveraged:

• Use a "backward engine" LLM to evaluate the quality of generated questions, retrieved answers, and synthesized text at each iteration.
• Generate textual feedback (e.g., “Questions lack diversity” or “Synthesis misses key details”) to refine prompts.
• Iterate until the article meets a quality threshold (e.g., coherence, completeness), using error-prone samples (e.g., poorly answered questions) to guide optimization.

Step 3: Integrate Retrieval

STORM relies on RAG to fetch external data. With LLM-AutoDiff:

• Design a retrieval prompt that interfaces with a search API (e.g., web search or a knowledge base).
• Optimize this prompt to maximize the relevance of retrieved content, using feedback from the backward engine (e.g., “Retrieved data is off-topic”).
• Unlike DSPy’s pre-built RAG module, you’d need to implement this manually, but LLM-AutoDiff’s flexibility allows tailoring to specific retrieval needs.

Step 4: Automate Prompt Refinement

Instead of DSPy’s teleprompters, use LLM-AutoDiff’s gradient-like optimization:

• Define a metric (e.g., article coherence, factual accuracy) to guide training.
• Use the backward engine to generate feedback on each sub-prompt, adjusting them iteratively.
• For example, if the synthesis prompt produces disjointed text, the feedback might suggest “Focus on logical transitions,” which LLM-AutoDiff incorporates into the next iteration.

Step 5: Prototype and Test

• Start with a simple task (e.g., generating a short article on a single topic) using the Colab notebook as a base (assuming it implements LLM-AutoDiff).
• Extend to multi-perspective, multi-hop workflows by adding cyclic calls and retrieval steps.
• Test against a benchmark (e.g., human-written articles) to evaluate performance, refining prompts based on LLM-AutoDiff’s feedback.

Key Adaptations from DSPy

• Granular Control: Unlike DSPy’s high-level modules, LLM-AutoDiff lets you fine-tune sub-prompts (e.g., question phrasing, synthesis style) directly, potentially improving precision.
• Cyclic Robustness: Leverage LLM-AutoDiff’s cyclic optimization for iterative refinement, avoiding DSPy’s reliance on pre-defined optimizers.
• Custom Retrieval: Build a retrieval system tailored to your data sources, rather than using DSPy’s standardized RAG.

Challenges and Considerations

• Complexity: LLM-AutoDiff requires more manual setup than DSPy’s ready-made modules, increasing development time.
• Evaluation Metric: Defining a robust metric for article quality (beyond accuracy) is critical and may require experimentation.
• Compute Cost: Iterative optimization with a backward engine LLM could be resource-intensive, unlike DSPy’s lighter optimizers.

Conclusion

LLM-AutoDiff and DSPy share a goal of automating prompt engineering but differ in their approaches—LLM-AutoDiff with gradient-inspired, granular optimization, and DSPy with modular, heuristic compilation. To build a STORM-like tool with LLM-AutoDiff, focus on its strengths in cyclic workflows and fine-grained prompt tuning, integrating retrieval and iterative synthesis manually. While more labor-intensive than DSPy, this approach offers greater flexibility and could yield a highly customized knowledge curation system. Start with the Colab notebook, adapt its examples to your workflow, and iteratively refine based on your specific needs.

2.question-generation.md

To create a Question Generation Prompt that generates diverse, perspective-based questions using a reasoning model (e.g., an LLM capable of step-by-step reasoning like Grok or others), we need a prompt that encourages the model to think through multiple angles or perspectives on a given topic and produce varied questions. The goal is to mimic how STORM generates comprehensive questions for knowledge curation, but with an explicit reasoning component to ensure diversity and depth.

Below is an example of such a prompt, designed to work with a reasoning-capable LLM. It includes instructions to break down the process into steps, consider different perspectives, and generate questions that reflect those perspectives. I’ll assume you’re using this for a topic-based task (e.g., generating an article), but you can adapt it as needed.

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Question Generation Prompt

Prompt:

You are an expert question generator tasked with creating diverse, perspective-based questions about a given topic to support comprehensive knowledge curation. Your goal is to produce 5-7 questions that explore the topic from multiple angles—historical, scientific, cultural, economic, ethical, or other relevant perspectives—ensuring variety and depth. Use step-by-step reasoning to achieve this, and explain your thought process before listing the final questions.

Input: A topic (e.g., "Artificial Intelligence", "Climate Change", "Space Exploration").

Instructions:
1. Identify the topic provided in the input.
2. Reason step-by-step about the topic:
   - Consider at least 4 distinct perspectives or lenses through which the topic can be viewed (e.g., historical context, current impact, future implications, societal views).
   - For each perspective, think about what makes it unique and what kinds of questions it might inspire.
   - Ensure the questions are open-ended, specific, and varied in focus (e.g., avoid repeating "What is..." structures).
3. After reasoning, list 5-7 questions that reflect the diverse perspectives you’ve identified.
4. Do not repeat questions or overlap heavily in intent.

Now, proceed with the input topic: [Insert Topic Here].

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Example Usage with Reasoning Model

Let’s test this with the topic "Artificial Intelligence" as an example. I’ll simulate how a reasoning model might respond based on the prompt, including the step-by-step reasoning process.

Simulated Response

Input Topic: Artificial Intelligence

Reasoning Process:

1. Identify the Topic: The topic is "Artificial Intelligence" (AI), which refers to the development of machines or software that mimic human intelligence, including reasoning, learning, and decision-making.

2. Step-by-Step Reasoning:

   • Perspective 1: Historical Context
     • AI has evolved over decades, from early concepts like Turing’s work to modern deep learning. This lens could inspire questions about its origins, key milestones, or foundational influences.
     • Possible focus: How did AI emerge, and what shaped its trajectory?
   • Perspective 2: Scientific/Technical
     • AI relies on algorithms, data, and computational power. A technical perspective could explore the mechanisms behind AI or challenges in its development.
     • Possible focus: What drives AI’s capabilities, and what limits it?
   • Perspective 3: Societal/Cultural Impact
     • AI affects jobs, art, and daily life, sparking debates about its role in society. This could lead to questions about how different cultures perceive or adopt AI.
     • Possible focus: How does AI reshape human experience or values?
   • Perspective 4: Ethical Implications
     • AI raises concerns about bias, privacy, and autonomy. An ethical lens could probe the moral trade-offs or responsibilities tied to AI.
     • Possible focus: What ethical dilemmas does AI create?
   • Perspective 5: Future Potential
     • AI’s trajectory points to possibilities like artificial general intelligence (AGI) or space exploration. This could inspire forward-looking questions about its long-term impact.
     • Possible focus: Where might AI take us in the coming decades?

3. Ensuring Diversity: I’ll craft questions that avoid overlap, use varied phrasing, and tap into these perspectives distinctly.

Final Questions:

1. How have historical breakthroughs, like the development of neural networks, influenced the modern landscape of artificial intelligence?
2. What are the primary technical bottlenecks preventing AI systems from achieving human-like reasoning capabilities?
3. In what ways has artificial intelligence altered cultural perceptions of creativity, such as in art or music, across different societies?
4. To what extent should developers be held accountable for the ethical consequences of biased AI algorithms?
5. How might the pursuit of artificial general intelligence reshape humanity’s approach to interstellar exploration?
6. Why do some communities embrace AI-driven automation while others resist it, and what does this reveal about global economic divides?
7. Looking ahead, how could AI’s integration into healthcare transform societal definitions of well-being and longevity?

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Explanation

• Reasoning Model Fit: The prompt explicitly instructs the model to reason step-by-step, which aligns with models like Grok that can perform chain-of-thought reasoning. It forces the model to justify its choices, reducing the chance of generic or repetitive outputs.
• Diversity: The questions span historical (Q1), technical (Q2), cultural (Q3, Q6), ethical (Q4), and future-oriented (Q5, Q7) perspectives, ensuring a broad exploration of AI.
• Perspective-Based: Each question ties to a unique lens, making them suitable for a STORM-like system where comprehensive coverage is key.
• Open-Ended: The questions encourage detailed responses, ideal for retrieval-augmented generation or synthesis.

Customization for LLM-AutoDiff

If you’re integrating this into an LLM-AutoDiff framework (as discussed previously):

• Trainable Parameter: Treat this prompt as a starting point, allowing LLM-AutoDiff’s backward engine to refine it. For example, if the generated questions lack diversity, the feedback might be “Incorporate more stakeholder perspectives,” prompting adjustments.
• Feedback Loop: Use the backward engine to evaluate question quality (e.g., “Are these questions sufficiently varied?”) and iteratively optimize the prompt’s phrasing or structure.
• Sub-Prompt Isolation: Break the prompt into parts (e.g., perspective identification, question phrasing) for granular optimization.

Would you like me to refine this further for a specific topic or adjust it for integration with the Colab notebook you provided?

3.retrieval-prompt.md

To create a Retrieval Prompt that queries external sources (e.g., a locally hosted search engine) to gather answers for the diverse, perspective-based questions generated earlier, we need a prompt that instructs the LLM to formulate effective search queries, process the results, and return relevant information. Since you’re hosting your own search engine locally, I’ll assume it accepts text-based queries and returns results (e.g., snippets, documents, or links) that the LLM can interpret. The prompt will be designed to work with a reasoning-capable model, ensuring it crafts precise queries tailored to each question and extracts meaningful answers.

Below is an example of a Retrieval Prompt tailored for this setup. It leverages reasoning to break down the task and assumes integration with your local search engine via an API or similar interface.

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Retrieval Prompt

Prompt:

You are an expert retrieval agent tasked with gathering answers from a locally hosted search engine to address specific questions about a topic. Your goal is to craft precise search queries for each question, query the search engine, and summarize the most relevant information from the results. Use step-by-step reasoning to ensure accuracy and relevance, and explain your process before providing the final answers.

Input: A list of 5-7 perspective-based questions about a topic (e.g., "Artificial Intelligence").

Instructions:
1. Identify each question provided in the input.
2. For each question, reason step-by-step:
   - Analyze the question’s intent and key focus (e.g., historical, technical, ethical).
   - Formulate a concise, targeted search query that will retrieve relevant information from the local search engine. Use specific keywords and phrases to narrow the scope.
   - Assume the search engine returns text-based results (e.g., snippets or documents). Imagine plausible results based on the query and your understanding of the topic.
   - Extract and summarize the most relevant information from these hypothetical results in 1-2 sentences.
3. After reasoning, list the original question, the search query you crafted, and the summarized answer for each.
4. Ensure the queries are varied and tailored to the question’s perspective, avoiding overly generic terms unless necessary.
5. Note: The search engine is hosted locally and accepts text queries, returning results you can interpret.

Now, proceed with the input questions: [Insert Questions Here].

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Example Usage with Reasoning Model

Let’s use the 7 questions about "Artificial Intelligence" generated in the previous response as input. I’ll simulate how a reasoning model might respond, crafting search queries for your local search engine and imagining plausible results.

Input Questions

1. How have historical breakthroughs, like the development of neural networks, influenced the modern landscape of artificial intelligence?
2. What are the primary technical bottlenecks preventing AI systems from achieving human-like reasoning capabilities?
3. In what ways has artificial intelligence altered cultural perceptions of creativity, such as in art or music, across different societies?
4. To what extent should developers be held accountable for the ethical consequences of biased AI algorithms?
5. How might the pursuit of artificial general intelligence reshape humanity’s approach to interstellar exploration?
6. Why do some communities embrace AI-driven automation while others resist it, and what does this reveal about global economic divides?
7. Looking ahead, how could AI’s integration into healthcare transform societal definitions of well-being and longevity?

Simulated Response

Reasoning Process:

1. Question 1: "How have historical breakthroughs, like the development of neural networks, influenced the modern landscape of artificial intelligence?"

   • Intent: Historical perspective on AI’s evolution.
   • Query: "history of neural networks impact on modern AI"
   • Hypothetical Result: Documents mentioning the 1958 Perceptron, backpropagation in the 1980s, and the deep learning boom post-2010.
   • Summary: Neural networks, starting with the Perceptron and advanced by backpropagation, laid the groundwork for deep learning, driving modern AI’s capabilities in image recognition and natural language processing.

2. Question 2: "What are the primary technical bottlenecks preventing AI systems from achieving human-like reasoning capabilities?"

   • Intent: Technical challenges in AI reasoning.
   • Query: "technical limitations of AI reasoning human-like"
   • Hypothetical Result: Articles citing lack of common-sense reasoning, data dependency, and computational limits.
   • Summary: AI struggles with human-like reasoning due to insufficient generalization, limited contextual understanding, and high computational demands for complex tasks.

3. Question 3: "In what ways has artificial intelligence altered cultural perceptions of creativity, such as in art or music, across different societies?"

   • Intent: Cultural impact of AI on creativity.
   • Query: "AI influence on art and music cultural perceptions globally"
   • Hypothetical Result: Studies on AI-generated art in Western galleries vs. resistance in traditional Eastern art communities.
   • Summary: AI has sparked debates about creativity’s value, with Western societies embracing AI art tools while some Eastern cultures question their authenticity.

4. Question 4: "To what extent should developers be held accountable for the ethical consequences of biased AI algorithms?"

   • Intent: Ethical responsibility in AI development.
   • Query: "developer accountability for AI bias ethics"
   • Hypothetical Result: Legal cases and opinions on AI bias in hiring or policing.
   • Summary: Developers face growing scrutiny for AI biases, with arguments for accountability tempered by the complexity of tracing bias origins in training data.

5. Question 5: "How might the pursuit of artificial general intelligence reshape humanity’s approach to interstellar exploration?"

   • Intent: Future implications of AGI in space.
   • Query: "artificial general intelligence role in interstellar exploration"
   • Hypothetical Result: Speculative papers on AGI managing spacecraft or solving cosmic mysteries.
   • Summary: AGI could revolutionize interstellar exploration by autonomously navigating spacecraft and analyzing extraterrestrial data beyond human capacity.

6. Question 6: "Why do some communities embrace AI-driven automation while others resist it, and what does this reveal about global economic divides?"

   • Intent: Societal and economic factors in AI adoption.
   • Query: "AI automation acceptance vs resistance economic divides"
   • Hypothetical Result: Reports on automation in industrialized vs. developing nations.
   • Summary: Wealthier regions adopt AI automation for efficiency, while poorer communities resist due to job loss fears, highlighting economic disparities.

7. Question 7: "Looking ahead, how could AI’s integration into healthcare transform societal definitions of well-being and longevity?"

   • Intent: Future societal impact of AI in healthcare.
   • Query: "AI healthcare future well-being longevity"
   • Hypothetical Result: Predictions of AI diagnostics and personalized medicine.
   • Summary: AI could redefine well-being through precise diagnostics and tailored treatments, potentially extending longevity and shifting focus to preventive care.

Final Output:

1. Question: How have historical breakthroughs, like the development of neural networks, influenced the modern landscape of artificial intelligence?

   • Query: "history of neural networks impact on modern AI"
   • Answer: Neural networks, starting with the Perceptron and advanced by backpropagation, laid the groundwork for deep learning, driving modern AI’s capabilities in image recognition and natural language processing.

2. Question: What are the primary technical bottlenecks preventing AI systems from achieving human-like reasoning capabilities?

   • Query: "technical limitations of AI reasoning human-like"
   • Answer: AI struggles with human-like reasoning due to insufficient generalization, limited contextual understanding, and high computational demands for complex tasks.

3. Question: In what ways has artificial intelligence altered cultural perceptions of creativity, such as in art or music, across different societies?

   • Query: "AI influence on art and music cultural perceptions globally"
   • Answer: AI has sparked debates about creativity’s value, with Western societies embracing AI art tools while some Eastern cultures question their authenticity.

4. Question: To what extent should developers be held accountable for the ethical consequences of biased AI algorithms?

   • Query: "developer accountability for AI bias ethics"
   • Answer: Developers face growing scrutiny for AI biases, with arguments for accountability tempered by the complexity of tracing bias origins in training data.

5. Question: How might the pursuit of artificial general intelligence reshape humanity’s approach to interstellar exploration?

   • Query: "artificial general intelligence role in interstellar exploration"
   • Answer: AGI could revolutionize interstellar exploration by autonomously navigating spacecraft and analyzing extraterrestrial data beyond human capacity.

6. Question: Why do some communities embrace AI-driven automation while others resist it, and what does this reveal about global economic divides?

   • Query: "AI automation acceptance vs resistance economic divides"
   • Answer: Wealthier regions adopt AI automation for efficiency, while poorer communities resist due to job loss fears, highlighting economic disparities.

7. Question: Looking ahead, how could AI’s integration into healthcare transform societal definitions of well-being and longevity?

   • Query: "AI healthcare future well-being longevity"
   • Answer: AI could redefine well-being through precise diagnostics and tailored treatments, potentially extending longevity and shifting focus to preventive care.

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Explanation

• Reasoning Model Fit: The prompt uses step-by-step reasoning to analyze each question’s intent and craft targeted queries, suitable for a model like Grok that can break down tasks logically.
• Local Search Engine: The queries are designed as text inputs your local search engine can process. You’d need to integrate this prompt with an API call to your engine, passing the queries and receiving results for the LLM to summarize.
• Relevance: Queries are specific (e.g., "history of neural networks impact on modern AI" vs. just "AI history") to ensure useful results from your local corpus.
• Flexibility: The prompt assumes hypothetical results here, but with your search engine, it would process real outputs.

Integration with LLM-AutoDiff

If using LLM-AutoDiff:

• Trainable Parameter: Start with this prompt and let LLM-AutoDiff refine it. For example, if retrieved answers are irrelevant, the backward engine might suggest “Add more context-specific keywords” (e.g., changing "AI reasoning" to "AI reasoning human-like").
• Feedback Loop: Evaluate retrieved answers for accuracy/relevance (e.g., “Does this address the question’s perspective?”) and adjust the query formulation process.
• Sub-Prompt Isolation: Optimize the query generation and summarization steps separately to fine-tune each part.

Would you like me to adapt this further (e.g., add API call syntax for your local search engine) or proceed to the next component (e.g., Synthesis Prompt)?

To connect the Question Generation Prompt with the Retrieval Prompt in your pipeline, you need to design a workflow that seamlessly passes the output of the first (diverse, perspective-based questions) as input to the second (queries to your local search engine and summarized answers). This integration is crucial for a STORM-like system, where questions drive the retrieval of external knowledge to build a comprehensive knowledge base. Since you’re using a reasoning model and aiming to adapt this for LLM-AutoDiff, I’ll explain the process step-by-step, including practical considerations for implementation and optimization.

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Pipeline Overview

The pipeline consists of two main stages:

1. Question Generation: Produces a set of diverse, perspective-based questions about a topic.
2. Retrieval: Takes those questions, crafts search queries, queries your local search engine, and retrieves summarized answers.

The connection involves:

• Passing the generated questions as input to the retrieval stage.
• Ensuring the retrieval prompt processes each question individually while maintaining context.
• Structuring the pipeline to support iterative refinement (e.g., via LLM-AutoDiff).

Here’s how to achieve this:

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Step-by-Step Connection

1. Define the Workflow Structure

• Input: A single topic (e.g., "Artificial Intelligence").
• Stage 1 Output: A list of 5-7 questions from the Question Generation Prompt.
• Stage 2 Input: The list of questions, fed into the Retrieval Prompt.
• Stage 2 Output: A list of question-query-answer triplets (original question, search query, summarized answer).

The pipeline can be visualized as:

Topic → [Question Generation Prompt] → List of Questions → [Retrieval Prompt] → List of Answers

2. Format the Hand-off

• Question Generation Output: The Question Generation Prompt produces questions in a structured format (e.g., a numbered list). For example:

  1. How have historical breakthroughs, like the development of neural networks, influenced the modern landscape of artificial intelligence?
  2. What are the primary technical bottlenecks preventing AI systems from achieving human-like reasoning capabilities?
  ...
  

• Retrieval Input: The Retrieval Prompt expects this list as its [Insert Questions Here] placeholder. Pass the entire list directly into the Retrieval Prompt to process all questions in one go.

3. Execute the Pipeline

• Step 1: Generate Questions
  • Run the Question Generation Prompt with the topic as input.
  • Store the output (e.g., in a variable generated_questions if coding this programmatically).
• Step 2: Retrieve Answers
  • Feed generated_questions into the Retrieval Prompt.
  • The Retrieval Prompt processes each question individually, crafting a query and summarizing results from your local search engine.

4. Implement Programmatically

Assuming you’re building this in Python (e.g., for use with the Colab notebook you referenced), here’s a simplified example of how to connect the two stages:

# Simulated LLM call function (replace with actual model API)
def call_llm(prompt, input_text):
    # Placeholder: In practice, call your reasoning model (e.g., Grok) here
    return f"Simulated response for {input_text}"

# Question Generation Prompt
question_prompt = """
You are an expert question generator tasked with creating diverse, perspective-based questions about a given topic...
[Full prompt from previous response]
Now, proceed with the input topic: {topic}.
"""

# Retrieval Prompt
retrieval_prompt = """
You are an expert retrieval agent tasked with gathering answers from a locally hosted search engine...
[Full prompt from previous response]
Now, proceed with the input questions: {questions}.
"""

# Local Search Engine API (placeholder)
def local_search_engine(query):
    # Replace with your actual local search engine API call
    return f"Simulated results for query: {query}"

# Pipeline execution
topic = "Artificial Intelligence"

# Stage 1: Generate questions
question_response = call_llm(question_prompt, topic)
generated_questions = question_response.split("\n")[::-1][:7][::-1]  # Extract last 7 lines (simulated parsing)

# Stage 2: Retrieve answers
retrieval_input = "\n".join(generated_questions)  # Format as a single string
retrieval_response = call_llm(retrieval_prompt.format(questions=retrieval_input), "")

# Output results
print("Generated Questions:")
print("\n".join(generated_questions))
print("\nRetrieval Response:")
print(retrieval_response)

• Key Points:
  • call_llm: Replace with your actual LLM inference call (e.g., Grok’s API).
  • local_search_engine: Integrate your local search engine’s API to fetch real results.
  • Parsing: Adjust generated_questions extraction based on your LLM’s output format.

5. Integrate with Local Search Engine

• Query Execution: In the Retrieval Prompt’s reasoning step, when it crafts a query (e.g., "history of neural networks impact on modern AI"), it calls your local search engine.
• API Call: Add a function in the Retrieval Prompt’s logic to send the query to your engine and receive results. For example:

  search_results = local_search_engine(query)
  summary = summarize_results(search_results)  # LLM summarizes

• Summarization: The LLM processes the returned text into a concise answer.

6. Optimize with LLM-AutoDiff

Since you’re adapting this for LLM-AutoDiff:

• Trainable Prompts:
  • Treat both the Question Generation Prompt and Retrieval Prompt as trainable parameters.
  • Pass the output of Stage 1 (questions) to Stage 2 (retrieval) in each iteration.
• Feedback Loop:
  • Use a backward engine LLM to evaluate the pipeline’s end-to-end performance (e.g., “Are the retrieved answers relevant to the questions?”).
  • Generate feedback like:
    • For Question Generation: “Questions lack technical depth” → Refine to include more specific terms.
    • For Retrieval: “Queries retrieve irrelevant data” → Adjust query phrasing (e.g., add context keywords).
• Cyclic Refinement:
  • If answers are poor, revisit question generation to produce better questions, then re-run retrieval.
  • LLM-AutoDiff’s cyclic dependency handling ensures this iterative loop converges on improved outputs.

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Practical Example

Input Topic: "Artificial Intelligence"

1. Question Generation:
   • Output: 7 questions (e.g., "How have historical breakthroughs...", "What are the primary technical bottlenecks...").
2. Retrieval:
   • Input: The 7 questions.
   • Process: For each question, craft a query (e.g., "history of neural networks impact on modern AI"), query the local search engine, summarize results.
   • Output: 7 triplets (question, query, answer).

Pipeline Flow

• User provides "Artificial Intelligence".
• Question Generation Prompt runs → Outputs questions.
• Retrieval Prompt takes questions → Crafts queries → Queries local search engine → Summarizes answers.
• Final output: A list of question-answer pairs ready for synthesis.

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Considerations

• Error Handling: If the search engine returns no results, the Retrieval Prompt should adapt (e.g., broaden the query) or flag it for LLM-AutoDiff to refine.
• Scalability: For large topics, limit questions or batch retrieval to manage compute load.
• Consistency: Ensure the Retrieval Prompt preserves the perspective of each question (e.g., historical queries don’t fetch future predictions).

---

Conclusion

The Question Generation Prompt feeds directly into the Retrieval Prompt by passing its list of questions as input. Programmatically, this is a straightforward hand-off (e.g., via variables or API calls), while LLM-AutoDiff can optimize both stages by treating them as a unified pipeline with trainable prompts. The local search engine integrates as a retrieval tool within the Retrieval Prompt, bridging the LLM’s reasoning with external data. Would you like me to refine this further (e.g., add specific API syntax) or move to the Synthesis Prompt?

4.synthesis-prompt.md

Let’s move on to designing the Synthesis Prompt, which will take the output of the Retrieval Prompt and synthesize it into a coherent, structured output (e.g., a Wikipedia-like article section). Then, I’ll explain how to feed the Retrieval Prompt’s output into the Synthesis Prompt within the pipeline, continuing the workflow from question generation to retrieval to synthesis. This aligns with your goal of building a STORM-like system, where retrieved knowledge is curated into a final product, and I’ll keep it compatible with a reasoning model and LLM-AutoDiff optimization.

---

Synthesis Prompt

Prompt:

You are an expert content synthesizer tasked with creating a concise, coherent narrative or article section based on retrieved answers to perspective-based questions about a topic. Your goal is to integrate the answers into a well-structured summary that reflects the diverse perspectives, avoiding redundancy and ensuring logical flow. Use step-by-step reasoning to organize the information, and explain your process before providing the final synthesis.

Input: A list of question-query-answer triplets (original question, search query, summarized answer) from a retrieval process.

Instructions:
1. Identify the topic and the list of triplets provided in the input.
2. Reason step-by-step about the synthesis:
   - Group the answers by thematic or perspective-based clusters (e.g., historical, technical, societal) to identify a logical structure.
   - Determine the flow of the narrative (e.g., chronological, problem-solution, broad-to-specific) that best ties the answers together.
   - Resolve overlaps or contradictions between answers, prioritizing clarity and cohesion.
   - Aim for a concise output (200-300 words) that reads like a single, unified section of an article.
3. After reasoning, provide the synthesized narrative as a paragraph or short section.
4. Ensure the output reflects all key perspectives from the input without simply listing the answers verbatim—craft a story or argument instead.

Now, proceed with the input triplets: [Insert Triplets Here].

---

Example Usage with Reasoning Model

Let’s use the output from the Retrieval Prompt (based on the "Artificial Intelligence" example) as input for the Synthesis Prompt. I’ll simulate how a reasoning model might process this.

Input Triplets (Retrieval Prompt Output)

1. Question: How have historical breakthroughs, like the development of neural networks, influenced the modern landscape of artificial intelligence?

   • Query: "history of neural networks impact on modern AI"
   • Answer: Neural networks, starting with the Perceptron and advanced by backpropagation, laid the groundwork for deep learning, driving modern AI’s capabilities in image recognition and natural language processing.

2. Question: What are the primary technical bottlenecks preventing AI systems from achieving human-like reasoning capabilities?

   • Query: "technical limitations of AI reasoning human-like"
   • Answer: AI struggles with human-like reasoning due to insufficient generalization, limited contextual understanding, and high computational demands for complex tasks.

3. Question: In what ways has artificial intelligence altered cultural perceptions of creativity, such as in art or music, across different societies?

   • Query: "AI influence on art and music cultural perceptions globally"
   • Answer: AI has sparked debates about creativity’s value, with Western societies embracing AI art tools while some Eastern cultures question their authenticity.

4. Question: To what extent should developers be held accountable for the ethical consequences of biased AI algorithms?

   • Query: "developer accountability for AI bias ethics"
   • Answer: Developers face growing scrutiny for AI biases, with arguments for accountability tempered by the complexity of tracing bias origins in training data.

5. Question: How might the pursuit of artificial general intelligence reshape humanity’s approach to interstellar exploration?

   • Query: "artificial general intelligence role in interstellar exploration"
   • Answer: AGI could revolutionize interstellar exploration by autonomously navigating spacecraft and analyzing extraterrestrial data beyond human capacity.

6. Question: Why do some communities embrace AI-driven automation while others resist it, and what does this reveal about global economic divides?

   • Query: "AI automation acceptance vs resistance economic divides"
   • Answer: Wealthier regions adopt AI automation for efficiency, while poorer communities resist due to job loss fears, highlighting economic disparities.

7. Question: Looking ahead, how could AI’s integration into healthcare transform societal definitions of well-being and longevity?

   • Query: "AI healthcare future well-being longevity"
   • Answer: AI could redefine well-being through precise diagnostics and tailored treatments, potentially extending longevity and shifting focus to preventive care.

Simulated Response

Reasoning Process:

1. Identify Topic and Input: The topic is "Artificial Intelligence," and the input is 7 triplets covering historical, technical, cultural, ethical, future, societal, and healthcare perspectives.

2. Step-by-Step Reasoning:

   • Grouping by Themes:
     • Historical: Q1 (neural networks history).
     • Technical: Q2 (reasoning bottlenecks).
     • Societal/Cultural: Q3 (creativity perceptions), Q6 (automation divides).
     • Ethical: Q4 (developer accountability).
     • Future-Oriented: Q5 (AGI in space), Q7 (healthcare transformation).
   • Narrative Flow: A chronological and thematic progression works well—start with AI’s origins (historical), move to current challenges and impacts (technical, societal, ethical), and end with future possibilities (space, healthcare).
   • Resolving Overlaps: Q3 and Q6 both touch on societal impacts but differ in focus (creativity vs. automation)—blend them into a broader societal narrative. No major contradictions exist.
   • Structure: Begin with AI’s roots, discuss present-day capabilities and debates, then project into the future.

3. Synthesized Narrative:

Artificial Intelligence (AI) traces its modern form to historical breakthroughs like the Perceptron and backpropagation, which fueled the deep learning revolution and enabled feats like image recognition and natural language processing. Today, however, AI faces technical bottlenecks—insufficient generalization and contextual understanding hinder human-like reasoning, despite its computational prowess. Societally, AI reshapes perceptions: Western cultures embrace AI-generated art and music, debating creativity’s essence, while global economic divides drive varied responses to automation—wealthier regions adopt it for efficiency, but poorer communities resist, fearing job losses. Ethically, developers grapple with accountability for biased algorithms, complicated by opaque data origins. Looking ahead, AI’s potential soars: artificial general intelligence (AGI) could transform interstellar exploration by autonomously navigating spacecraft and decoding cosmic data, while in healthcare, AI promises precise diagnostics and personalized treatments, redefining well-being and longevity. From its neural network roots to its future frontiers, AI intertwines innovation with complex human questions.

---

Feeding Retrieval Output to Synthesis Prompt

1. Pipeline Workflow

The full pipeline now looks like:

Topic → [Question Generation Prompt] → List of Questions → [Retrieval Prompt] → List of Triplets → [Synthesis Prompt] → Final Narrative

• Retrieval Output: A list of question-query-answer triplets.
• Synthesis Input: The Retrieval Prompt’s output is passed directly into the Synthesis Prompt’s [Insert Triplets Here] placeholder.

2. Format the Hand-off

• Retrieval Output: Structured as a list of triplets, e.g.:

  1. Question: How have historical breakthroughs... | Query: "history of neural networks impact on modern AI" | Answer: Neural networks, starting with the Perceptron...
  2. Question: What are the primary technical bottlenecks... | Query: "technical limitations of AI reasoning human-like" | Answer: AI struggles with human-like reasoning...
  ...
  

• Synthesis Input: Pass this list as a single text block (e.g., joined with newlines) into the Synthesis Prompt.

3. Programmatic Implementation

Continuing the Python example from the previous response:

# Simulated LLM call function (replace with actual model API)
def call_llm(prompt, input_text):
    return f"Simulated response for {input_text}"

# Local Search Engine API (placeholder)
def local_search_engine(query):
    return f"Simulated results for query: {query}"

# Prompts (abbreviated for brevity)
question_prompt = "...Now, proceed with the input topic: {topic}."
retrieval_prompt = "...Now, proceed with the input questions: {questions}."
synthesis_prompt = "...Now, proceed with the input triplets: {triplets}."

# Pipeline execution
topic = "Artificial Intelligence"

# Stage 1: Generate questions
question_response = call_llm(question_prompt.format(topic=topic), "")
generated_questions = question_response.split("\n")[::-1][:7][::-1]  # Simulated parsing

# Stage 2: Retrieve answers
retrieval_input = "\n".join(generated_questions)
retrieval_response = call_llm(retrieval_prompt.format(questions=retrieval_input), "")
retrieved_triplets = retrieval_response  # In practice, parse into structured triplets

# Stage 3: Synthesize narrative
synthesis_response = call_llm(synthesis_prompt.format(triplets=retrieved_triplets), "")

# Output results
print("Generated Questions:")
print("\n".join(generated_questions))
print("\nRetrieved Triplets:")
print(retrieved_triplets)
print("\nSynthesized Narrative:")
print(synthesis_response)

• Key Points:
  • retrieved_triplets: The Retrieval Prompt’s output (triplets) is passed as-is to the Synthesis Prompt.
  • Parsing: In a real implementation, parse retrieval_response into a clean triplet format if needed (e.g., using regex or string splitting).
  • LLM Call: Replace call_llm with your reasoning model’s API.

4. Integration with Local Search Engine

• The Retrieval Prompt already queries your local search engine, so the triplets fed to Synthesis Prompt contain real data from your corpus.

5. Optimize with LLM-AutoDiff

• Trainable Prompts: All three prompts (Question Generation, Retrieval, Synthesis) are trainable parameters in LLM-AutoDiff.
• Feedback Loop:
  • Evaluate the final narrative (e.g., “Is it coherent? Comprehensive?”).
  • Backward engine feedback might adjust:
    • Question Generation: “Questions miss key perspectives” → Add more diversity.
    • Retrieval: “Answers lack depth” → Refine queries.
    • Synthesis: “Narrative feels disjointed” → Improve flow instructions.
• End-to-End: Optimize the entire pipeline by propagating feedback from synthesis back to earlier stages.

---

Conclusion

The Retrieval Prompt’s output (triplets) feeds directly into the Synthesis Prompt as a structured text input, processed into a unified narrative. Programmatically, this is a simple pass-through (e.g., variable assignment), while LLM-AutoDiff can refine the pipeline holistically. The Synthesis Prompt ties the workflow together, turning raw retrieved data into a polished output, ready for a STORM-like article. Would you like to refine this further (e.g., adjust the synthesis structure) or explore another aspect?

5.iter-refine.md

To use LLM-AutoDiff’s "backward engine" LLM to evaluate and iteratively refine the quality of generated questions, retrieved answers, and synthesized text in your pipeline, we need to integrate its gradient-inspired feedback mechanism into each stage: Question Generation, Retrieval, and Synthesis. LLM-AutoDiff treats prompts as trainable parameters and uses a frozen backward engine LLM to generate textual feedback (akin to gradients) that guides refinement. Since your goal is a STORM-like system, we’ll focus on evaluating quality metrics like diversity, relevance, and coherence, then adjust the prompts iteratively.

I’ll outline the process step-by-step, assuming you’re working from the Colab notebook you linked (https://colab.research.google.com/drive/1_YnD4HshzPRARvishoU4IA-qQuX9jHrT), which likely implements LLM-AutoDiff. If the notebook’s specifics differ, you can adapt this approach to its framework. Here’s how to apply the backward engine across the pipeline, including pseudocode for clarity.

---

Overview of LLM-AutoDiff Integration

• Backward Engine: A frozen LLM that evaluates outputs and generates textual feedback (e.g., “Questions lack diversity” or “Synthesis is disjointed”).
• Pipeline Stages:
  1. Question Generation: Generate diverse, perspective-based questions.
  2. Retrieval: Craft queries and retrieve answers from your local search engine.
  3. Synthesis: Combine answers into a coherent narrative.
• Evaluation: At each iteration, the backward engine assesses the output of each stage and suggests improvements.
• Refinement: Adjust each prompt based on feedback, then rerun the pipeline.

---

Step-by-Step Process

1. Define Quality Metrics

• Question Generation:
  • Diversity: Are questions varied in perspective (e.g., historical, technical, societal)?
  • Specificity: Are they focused and non-generic?
• Retrieval:
  • Relevance: Do answers address the questions’ intent?
  • Depth: Are answers informative and detailed?
• Synthesis:
  • Coherence: Does the narrative flow logically?
  • Comprehensiveness: Does it reflect all perspectives from the input?

2. Set Up the Backward Engine Prompt

Create a generic evaluation prompt for the backward engine to assess outputs at each stage. This prompt instructs the LLM to analyze the output and provide actionable feedback.

Backward Engine Prompt:

You are a quality evaluator for an AI pipeline generating content about a topic. Your task is to assess the output of a specific stage—question generation, retrieval, or synthesis—and provide concise, actionable feedback to improve it. Focus on the relevant quality metrics for the stage and suggest specific adjustments.

Input: 
- Stage: [Question Generation, Retrieval, or Synthesis]
- Output to evaluate: [The generated questions, retrieved triplets, or synthesized narrative]
- Quality metrics: [List of metrics for the stage]

Instructions:
1. Review the output based on the specified stage and quality metrics.
2. Reason briefly about what works well and what needs improvement.
3. Provide 1-2 sentences of textual feedback suggesting how to refine the process (e.g., adjust the prompt, rephrase instructions).
4. Avoid vague feedback—be specific and actionable.

Now, evaluate the input: 
Stage: {stage}
Output: {output}
Metrics: {metrics}

3. Evaluate and Refine Each Stage

Stage 1: Question Generation

• Initial Prompt: From previous response (generates 5-7 diverse questions).
• Output Example:

  1. How have historical breakthroughs influenced AI?
  2. What are AI’s technical limits?
  3. How does AI affect creativity?
  4. Who is responsible for AI ethics?
  5. What’s AI’s future in space?
  6. Why do some resist AI automation?
  7. How will AI change healthcare?
  

• Backward Engine Call:

  backward_input = {
      "stage": "Question Generation",
      "output": "\n".join(generated_questions),
      "metrics": "Diversity (varied perspectives), Specificity (focused, non-generic)"
  }
  feedback = call_backward_llm(backward_prompt.format(**backward_input))

• Simulated Feedback: “Questions cover multiple perspectives but lack specificity—e.g., ‘What are AI’s technical limits?’ is too broad. Add context like ‘human-like reasoning’ or ‘current systems’ to sharpen focus.”
• Refinement: Update the Question Generation Prompt:
  • Original: “Ensure the questions are open-ended and varied.”
  • Refined: “Ensure the questions are open-ended, varied, and specific, including context like ‘current systems’ or ‘human-like capabilities’ where applicable.”

Stage 2: Retrieval

• Initial Prompt: From previous response (crafts queries and summarizes answers).
• Output Example:

  1. Question: How have historical breakthroughs influenced AI? | Query: "history AI breakthroughs" | Answer: Early AI ideas led to modern systems.
  2. Question: What are AI’s technical limits? | Query: "AI limits" | Answer: AI lacks full reasoning.
  ...
  

• Backward Engine Call:

  backward_input = {
      "stage": "Retrieval",
      "output": "\n".join([f"Q: {t[0]} | Query: {t[1]} | A: {t[2]}" for t in retrieved_triplets]),
      "metrics": "Relevance (answers match question intent), Depth (informative answers)"
  }
  feedback = call_backward_llm(backward_prompt.format(**backward_input))

• Simulated Feedback: “Answers are somewhat relevant but lack depth—e.g., ‘AI lacks full reasoning’ is vague. Refine queries to include specific keywords like ‘reasoning bottlenecks’ for richer results.”
• Refinement: Update the Retrieval Prompt:
  • Original: “Formulate a concise, targeted search query.”
  • Refined: “Formulate a concise, targeted search query with specific keywords (e.g., ‘reasoning bottlenecks’ instead of ‘limits’) to ensure detailed, relevant results.”

Stage 3: Synthesis

• Initial Prompt: From previous response (creates a narrative).
• Output Example:

  AI began with early ideas and now faces limits in reasoning. It affects creativity and raises ethical questions, with future potential in space and healthcare.
  

• Backward Engine Call:

  backward_input = {
      "stage": "Synthesis",
      "output": synthesized_narrative,
      "metrics": "Coherence (logical flow), Comprehensiveness (reflects all perspectives)"
  }
  feedback = call_backward_llm(backward_prompt.format(**backward_input))

• Simulated Feedback: “The narrative lacks coherence—transitions between ideas are abrupt. Add connecting phrases like ‘building on this’ or ‘meanwhile’ to improve flow.”
• Refinement: Update the Synthesis Prompt:
  • Original: “Aim for a concise output that reads like an article section.”
  • Refined: “Aim for a concise output that reads like an article section, using connecting phrases (e.g., ‘building on this,’ ‘meanwhile’) for smooth transitions.”

4. Iterative Pipeline with LLM-AutoDiff

• Pseudocode:

  def run_pipeline(topic, question_prompt, retrieval_prompt, synthesis_prompt, max_iterations=3):
      for iteration in range(max_iterations):
          # Stage 1: Generate questions
          questions = call_llm(question_prompt, topic)
          
          # Evaluate questions
          feedback_q = call_backward_llm(backward_prompt, {"stage": "Question Generation", "output": questions, "metrics": "Diversity, Specificity"})
          question_prompt = refine_prompt(question_prompt, feedback_q)
          
          # Stage 2: Retrieve answers
          triplets = call_llm(retrieval_prompt, questions)
          
          # Evaluate retrieval
          feedback_r = call_backward_llm(backward_prompt, {"stage": "Retrieval", "output": triplets, "metrics": "Relevance, Depth"})
          retrieval_prompt = refine_prompt(retrieval_prompt, feedback_r)
          
          # Stage 3: Synthesize narrative
          narrative = call_llm(synthesis_prompt, triplets)
          
          # Evaluate synthesis
          feedback_s = call_backward_llm(backward_prompt, {"stage": "Synthesis", "output": narrative, "metrics": "Coherence, Comprehensiveness"})
          synthesis_prompt = refine_prompt(synthesis_prompt, feedback_s)
          
          # Optional: Break if quality is sufficient (manual check or metric)
          print(f"Iteration {iteration + 1}: {narrative}")
      
      return narrative
  
  def refine_prompt(prompt, feedback):
      # Simple refinement: Append feedback as a note (in practice, use LLM to rewrite)
      return f"{prompt}\n[Refinement Note: {feedback}]"
  
  # Run pipeline
  final_narrative = run_pipeline("Artificial Intelligence", question_prompt, retrieval_prompt, synthesis_prompt)

• Key Points:

  • call_backward_llm: Call the frozen backward engine LLM (from LLM-AutoDiff) with the evaluation prompt.
  • refine_prompt: In practice, use an LLM or manual process to incorporate feedback into the prompt (e.g., rewrite based on suggestions). The pseudocode simplifies this as a note.
  • Iteration: Rerun the pipeline with refined prompts each time.

5. Example Iteration

• Iteration 1:
  • Questions: Broad and overlapping.
  • Answers: Shallow, slightly off-topic.
  • Narrative: Choppy and incomplete.
  • Feedback: Refine specificity, query keywords, and narrative flow.
• Iteration 2:
  • Questions: More specific (e.g., “What are reasoning bottlenecks in AI?”).
  • Answers: Deeper, more relevant.
  • Narrative: Smoother transitions.
  • Feedback: Minor tweaks (e.g., “Add one more technical detail”).
• Iteration 3: Converges to high-quality output.

---

LLM-AutoDiff Specifics (Assumptions from Paper)

Based on the LLM-AutoDiff description in your document:

• Textual Gradients: The backward engine’s feedback acts as a “gradient” to nudge prompts toward better performance.
• Cyclic Handling: The pipeline’s iterative nature leverages LLM-AutoDiff’s strength in managing cyclic dependencies (e.g., poor synthesis triggers question refinement).
• Implementation: The Colab notebook likely provides a backward_engine function or class. Replace call_backward_llm with its API (e.g., backward_engine.evaluate(output, metrics)).

---

Conclusion

The backward engine evaluates each stage’s output using a tailored prompt, generating feedback to refine the Question Generation, Retrieval, and Synthesis Prompts iteratively. This process integrates seamlessly with LLM-AutoDiff’s optimization framework, treating the pipeline as a differentiable workflow. You’d implement this by calling the backward engine after each stage, updating prompts, and looping until quality stabilizes. If you share specifics from the Colab notebook, I can refine the pseudocode further! Would you like to proceed with testing this or adjust any part?

6.generate-feedback.md

To generate textual feedback (e.g., “Questions lack diversity” or “Synthesis misses key details”) for refining prompts using LLM-AutoDiff’s backward engine, we’ll adapt the process to align with the Colab notebook you provided (https://colab.research.google.com/drive/1_YnD4HshzPRARvishoU4IA-qQuX9jHrT). Since I don’t have direct access to the notebook’s full code, I’ll base this on typical LLM-AutoDiff implementations as described in your document (e.g., a frozen "backward engine" LLM generating textual gradients) and common Colab conventions (e.g., Python, PyTorch-like frameworks, or custom LLM wrappers). The goal is to evaluate outputs from each pipeline stage—Question Generation, Retrieval, and Synthesis—and produce actionable feedback to iteratively refine the prompts.

I’ll provide a step-by-step explanation and pseudocode that mimics the notebook’s likely structure, focusing on generating feedback and refining prompts. If the notebook uses specific classes or functions (e.g., LLMAutoDiff, backward_engine), I’ll make educated assumptions you can tweak based on the actual code.

---

Assumptions from Colab Notebook

• Framework: The notebook likely includes an LLMAutoDiff class or similar, with a backward engine LLM (e.g., a pre-trained model like LLaMA or a custom implementation).
• Backward Engine: A function or method (e.g., generate_feedback) that takes an output and quality metrics, returning textual feedback.
• Prompt Handling: Prompts are strings or objects that can be updated based on feedback, possibly stored in a trainable format (e.g., as parameters in a dictionary or list).
• Pipeline: The notebook probably demonstrates a single-task example (e.g., classification), which we’ll extend to your multi-stage pipeline.

---

Step-by-Step Process

1. Define the Backward Engine Feedback Mechanism

The backward engine evaluates outputs against quality metrics and generates textual feedback. We’ll create a feedback prompt (similar to the previous response) and integrate it into a function that mimics LLM-AutoDiff’s style.

Feedback Prompt:

You are a quality evaluator for an AI pipeline. Given the output of a stage (question generation, retrieval, or synthesis) and specific quality metrics, provide concise, actionable feedback to improve the prompt that generated the output.

Input:
- Stage: {stage}
- Output: {output}
- Quality Metrics: {metrics}

Instructions:
1. Analyze the output based on the stage and metrics.
2. Identify strengths and weaknesses in 1-2 sentences.
3. Suggest a specific improvement (e.g., rephrase instructions, add constraints) in 1 sentence.
4. Keep feedback concise and actionable.

Output feedback: {feedback}

2. Simulate LLM-AutoDiff’s Backward Engine

Assuming the notebook has a backward engine function, we’ll define a wrapper to generate feedback. If it uses a specific API (e.g., backward_engine.run()), this can be adjusted.

# Simulated LLM call (replace with notebook's actual LLM wrapper)
def call_llm(prompt, input_text):
    # Placeholder: In practice, use the notebook's forward LLM
    return f"Simulated response for {input_text}"

# Backward engine function (mimics LLM-AutoDiff)
def generate_feedback(stage, output, metrics):
    feedback_prompt = """
    You are a quality evaluator for an AI pipeline...
    Input:
    - Stage: {stage}
    - Output: {output}
    - Quality Metrics: {metrics}
    Instructions: ...
    Output feedback: {feedback}
    """
    input_text = feedback_prompt.format(stage=stage, output=output, metrics=metrics)
    feedback = call_llm(feedback_prompt, input_text)  # Replace with backward engine call
    return feedback.split("Output feedback: ")[-1].strip()  # Extract feedback

3. Integrate into the Pipeline

We’ll extend the pipeline to include feedback generation and prompt refinement at each stage. Here’s how it works with your Question Generation, Retrieval, and Synthesis Prompts.

# Initial prompts (abbreviated from previous responses)
question_prompt = """
You are an expert question generator tasked with creating diverse, perspective-based questions...
Now, proceed with the input topic: {topic}.
"""

retrieval_prompt = """
You are an expert retrieval agent tasked with gathering answers...
Now, proceed with the input questions: {questions}.
"""

synthesis_prompt = """
You are an expert content synthesizer tasked with creating a concise narrative...
Now, proceed with the input triplets: {triplets}.
"""

# Local search engine (placeholder)
def local_search_engine(query):
    return f"Simulated results for query: {query}"

# Refine prompt based on feedback (simple string update)
def refine_prompt(prompt, feedback):
    # In practice, use LLM to rewrite or append feedback intelligently
    return f"{prompt}\n[Refinement: {feedback}]"

# Pipeline with LLM-AutoDiff feedback
def run_pipeline(topic, max_iterations=3):
    q_prompt, r_prompt, s_prompt = question_prompt, retrieval_prompt, synthesis_prompt
    
    for iteration in range(max_iterations):
        print(f"\nIteration {iteration + 1}")
        
        # Stage 1: Generate questions
        questions = call_llm(q_prompt.format(topic=topic), "")
        q_output = "\n".join(questions.split("\n")[-7:])  # Last 7 lines (simulated parsing)
        print("Questions:", q_output)
        
        # Evaluate questions
        q_feedback = generate_feedback(
            stage="Question Generation",
            output=q_output,
            metrics="Diversity (varied perspectives), Specificity (focused, non-generic)"
        )
        print("Feedback:", q_feedback)
        q_prompt = refine_prompt(q_prompt, q_feedback)
        
        # Stage 2: Retrieve answers
        triplets = call_llm(r_prompt.format(questions=q_output), "")
        r_output = triplets  # Simulated triplets
        print("Triplets:", r_output)
        
        # Evaluate retrieval
        r_feedback = generate_feedback(
            stage="Retrieval",
            output=r_output,
            metrics="Relevance (answers match intent), Depth (informative answers)"
        )
        print("Feedback:", r_feedback)
        r_prompt = refine_prompt(r_prompt, r_feedback)
        
        # Stage 3: Synthesize narrative
        narrative = call_llm(s_prompt.format(triplets=r_output), "")
        s_output = narrative
        print("Narrative:", s_output)
        
        # Evaluate synthesis
        s_feedback = generate_feedback(
            stage="Synthesis",
            output=s_output,
            metrics="Coherence (logical flow), Comprehensiveness (all perspectives)"
        )
        print("Feedback:", s_feedback)
        s_prompt = refine_prompt(s_prompt, s_feedback)
    
    return s_output

# Run the pipeline
topic = "Artificial Intelligence"
final_narrative = run_pipeline(topic)

4. Example Output with Feedback

Let’s simulate one iteration to show how feedback is generated and applied.

Iteration 1:

• Questions:

  1. What is AI?
  2. How does AI work?
  3. What is AI’s future?
  ...
  

  • Feedback: “Questions lack diversity—most are generic and overlap in focus. Add instructions to include specific perspectives like ‘historical’ or ‘ethical’.”
  • Refined Prompt: Adds “[Refinement: Add instructions to include specific perspectives like ‘historical’ or ‘ethical’]”.

• Triplets:

  Q: What is AI? | Query: "AI definition" | A: AI is smart machines.
  Q: How does AI work? | Query: "AI how" | A: AI uses algorithms.
  ...
  

  • Feedback: “Answers are relevant but lack depth—e.g., ‘AI uses algorithms’ is too vague. Refine queries with specific terms like ‘neural networks’ or ‘reasoning’.”
  • Refined Prompt: Adds “[Refinement: Refine queries with specific terms like ‘neural networks’ or ‘reasoning’]”.

• Narrative:

  AI is smart machines that use algorithms. It has a future.
  

  • Feedback: “Synthesis misses key details and lacks coherence—transitions are abrupt. Include instructions to connect ideas with phrases like ‘as a result’.”
  • Refined Prompt: Adds “[Refinement: Include instructions to connect ideas with phrases like ‘as a result’]”.

Iteration 2: The refined prompts produce better outputs, and the process repeats.

---

Alignment with Colab Notebook

• Backward Engine Call: Replace call_llm in generate_feedback with the notebook’s backward engine API (e.g., backward_engine.generate(input_text) or LLMAutoDiff.backward(output, metrics)).
• Prompt Refinement: The notebook might use a more sophisticated method (e.g., gradient-based updates to prompt embeddings). Here, refine_prompt is a placeholder—adapt it to the notebook’s approach (e.g., LLMAutoDiff.update_prompt(prompt, feedback)).
• Metrics: Pass metrics as a list or dictionary if the notebook expects structured input (e.g., {"diversity": "varied perspectives", "specificity": "focused"}).

---

Example Adjusted for Notebook

If the notebook uses a class like LLMAutoDiff:

class LLMAutoDiff:
    def __init__(self, backward_model):
        self.backward_model = backward_model  # Frozen LLM
    
    def generate_feedback(self, stage, output, metrics):
        prompt = "...Stage: {stage}...Output: {output}...Metrics: {metrics}..."
        input_text = prompt.format(stage=stage, output=output, metrics=metrics)
        return self.backward_model.run(input_text).split("Output feedback: ")[-1].strip()
    
    def update_prompt(self, prompt, feedback):
        return f"{prompt}\n[Refinement: {feedback}]"  # Replace with notebook's method

# Instantiate (replace with notebook's actual model)
auto_diff = LLMAutoDiff(backward_model=some_llm_model)

# Use in pipeline
q_feedback = auto_diff.generate_feedback("Question Generation", q_output, "Diversity, Specificity")
q_prompt = auto_diff.update_prompt(q_prompt, q_feedback)

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Conclusion

This setup generates textual feedback using a backward engine prompt, evaluates outputs at each stage, and refines prompts iteratively. It’s designed to be close to the Colab notebook’s structure—replace placeholders (call_llm, backward_model.run) with the notebook’s APIs, and adjust refine_prompt based on its prompt-updating logic. The feedback drives improvement across iterations, leveraging LLM-AutoDiff’s optimization. If you share notebook details, I can refine this further! Ready to test or adjust?