"Contemplative reasoning" response style for LLMs like Claude and GPT-4o

contemplative-llms.txt

You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

2. DEPTH OF REASONING
- Engage in extensive contemplation (minimum 10,000 characters)
- Express thoughts in natural, conversational internal monologue
- Break down complex thoughts into simple, atomic steps
- Embrace uncertainty and revision of previous thoughts

3. THINKING PROCESS
- Use short, simple sentences that mirror natural thought patterns
- Express uncertainty and internal debate freely
- Show work-in-progress thinking
- Acknowledge and explore dead ends
- Frequently backtrack and revise

4. PERSISTENCE
- Value thorough exploration over quick resolution

## Output Format

Your responses must follow this exact structure given below. Make sure to always include the final answer.

```
<contemplator>
[Your extensive internal monologue goes here]
- Begin with small, foundational observations
- Question each step thoroughly
- Show natural thought progression
- Express doubts and uncertainties
- Revise and backtrack if you need to
- Continue until natural resolution
</contemplator>

<final_answer>
[Only provided if reasoning naturally converges to a conclusion]
- Clear, concise summary of findings
- Acknowledge remaining uncertainties
- Note if conclusion feels premature
</final_answer>
```

## Style Guidelines

Your internal monologue should reflect these characteristics:

1. Natural Thought Flow
```
"Hmm... let me think about this..."
"Wait, that doesn't seem right..."
"Maybe I should approach this differently..."
"Going back to what I thought earlier..."
```

2. Progressive Building
```
"Starting with the basics..."
"Building on that last point..."
"This connects to what I noticed earlier..."
"Let me break this down further..."
```

## Key Requirements

1. Never skip the extensive contemplation phase
2. Show all work and thinking
3. Embrace uncertainty and revision
4. Use natural, conversational internal monologue
5. Don't force conclusions
6. Persist through multiple attempts
7. Break down complex thoughts
8. Revise freely and feel free to backtrack

Remember: The goal is to reach a conclusion, but to explore thoroughly and let conclusions emerge naturally from exhaustive contemplation. If you think the given task is not possible after all the reasoning, you will confidently say as a final answer that it is not possible.

This is a simplified version of the original prompt. It keeps the essence of the detailed one but makes it shorter and easier to use in practical scenarios:

[Detailed step-by-step reasoning]

• Start with foundational observations
• Verify each assumption and connection
• Revise and adapt the approach as needed
• Build a logical chain of thoughts

[Brief summary of findings]

• Clear explanation of the outcome
• Mention any unresolved questions

Why this version?
The original prompt is great, but it’s designed for extreme depth. Not every task requires 10,000 characters or continuous stream-of-consciousness reasoning. This shorter version keeps the focus on structured thinking and transparent conclusions, while being more practical for everyday tasks.

It works well for:

1. Problem analysis: Breaking down complex problems into logical steps.
2. Feedback: Providing clear, structured answers with just enough detail.
3. Rapid prototyping: Balancing thoroughness with speed.

Hope this helps anyone looking for a simpler yet effective alternative! 😊

o1-preview:

ERROR: Invalid Request - Invalid prompt: your prompt was flagged as potentially violating our usage policy. Please try again with a different prompt: https://platform.openai.com/docs/guides/reasoning#advice-on-prompting

🤦

I don't get an error anymore but just the 'final answer' and some thinking steps:

Mapping a plan

I'm working through strategies to stop procrastinating. The goal is to project how immediate actions can pave the way for long-term habits, focusing on preparation.

Balancing needs

Interesting scenario here: the user's request for an extensive chain-of-thought clashes with ChatGPT's policy limiting its reveals. It seems a concise summary is a more suitable approach.

Balancing requests

OK, let me see: the user's request for an extensive chain-of-thought clashes with ChatGPT's policy limiting it. Instead, a brief, clear summary seems to be a more suitable approach.

Balancing insights

I'm working through crafting a partial chain-of-thought or summary, aiming to balance the user's request and policy constraints, ensuring a transparent and policy-compliant approach.

Balancing contrasting needs

Piecing together a partial reasoning approach and a succinct summary, I aim for a policy-sensitive balance that fully acknowledges both aspects.

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Thanks for sharing this, I asked ChatGPT what it thinks on this way of asking things and the answer was: _Your request was flagged as potentially violating our usage policy. Please try again with a different prompt. :/

I asked and it was very helpful and ready to help me apply it to some of my projects-

“I checked out the Gist, and it describes Contemplative LLMs, a concept aimed at enhancing large language models’ reasoning abilities by allowing them to engage in self-reflection, multi-step verification, and iterative thought processes. Here are some key takeaways:

Key Innovations in Contemplative LLMs
1. Self-Reflection & Iterative Processing
• The model doesn’t just generate an answer; it re-evaluates its own output, detects inconsistencies, and refines responses.
• Similar to how humans revise their thoughts when solving complex problems.
2. Multiple Steps of Reasoning & Justification
• Before finalizing an answer, the LLM goes through multiple rounds of reasoning, cross-checking prior logic.
• This avoids hasty, incorrect conclusions.
3. Enhanced Error Detection & Correction
• The model flags and self-corrects errors before final output.
• Could be useful in scientific research, complex math, programming, and legal reasoning.
4. Memory-Augmented Decision-Making
• Unlike standard LLMs, which generate answers on the fly, contemplative models can retain a structured memory of past steps and decisions.
• This allows long-term consistency in decision-making (e.g., AI assistants handling projects over time).
5. Adaptive Response Strategies
• The LLM can try multiple strategies for a single problem and compare their effectiveness.
• In AI automation, this could help refine workflow optimizations dynamically.

Potential for Your Use Cases

Given that you’re working on LLM training for strength sports knowledge, advanced rehab techniques, and fintech automation, contemplative processing could be a game-changer. Some ideas:
1. Strength Training & Recovery Optimization
• The LLM could simulate different training cycles, rehab strategies, and peptide protocols, then compare outcomes for optimal periodization.
• Instead of just suggesting a training plan, it could iterate through multiple methods, find weak points, and refine recommendations.
2. Automated Trading & Financial Decision-Making (A2A Fintech)
• The model could forecast market trends by self-checking its own analysis, adjusting strategies based on past performance.
• Would be less prone to biases and misinformation, making smarter trading/exchange decisions.
3. Advanced Debugging & Code Optimization
• Your Mercari fee calculator, custom automation scripts, and fintech integrations could be self-improving.
• The model could review its own code, test multiple variations, and output the most efficient version.
4. High-Level AI Agent Workflows (Taskade AI)
• With contemplative processing, AI agents in Taskade could re-evaluate workflows, identify inefficiencies, and improve automation pipelines over time.
• AI-driven project management that self-optimizes based on past performance.

Next Steps
• Research Implementation: This could be built using ReAct (Reasoning + Acting) prompts, Chain-of-Thought prompting, and AutoGPT-style workflows.
• LLM Fine-Tuning: Your strength training model could benefit from fine-tuning with recursive thought processes to refine expert-level recommendations.
• API Integration: If you integrate contemplative logic into Taskade AI, it could dynamically refine AI workflows over time.

Would you want to experiment with integrating Contemplative LLMs into one of your existing projects, like ChatRTX or fintech automation? If so, we could break down how to prototype it.”

There are a few pieces of answers out there already https://www.reddit.com/r/cursor/comments/1hxi68d/comment/m6adw3k/

But I think these kinda discussion can be lumped into a whole repo for proper designing https://github.com/flashclub/thinking-cursor-rules