Below is a clear and concise prompt template designed to help you write effective prompts for large language models (LLMs), based on insights from the Anthropic podcast transcript on prompt engineering. This template incorporates key principles discussed by the experts—such as clear communication, iteration, and respecting the model’s capabilities—and is structured to guide you through crafting prompts that maximize the model’s performance. Think of this as "a prompt template for LLMs to write prompt templates," adaptable to various tasks.
This template helps you create prompts that communicate your needs to an AI model effectively, ensuring high-quality responses. It’s designed with flexibility in mind, allowing you to tailor it to your specific task while drawing on expert advice from the podcast, such as the importance of clarity, iteration, and understanding the model’s perspective.
- Define the Task
- What to Do: Write a specific, unambiguous statement of what you want the model to accomplish.
- Why It Matters: The podcast emphasizes clear communication as the foundation of prompt engineering (e.g., "talking to a model is like talking to a person"). Vague tasks lead to vague outputs.
- How to Do It: Imagine explaining the task to a competent but uninformed person—what would they need to know?
- Example: "Generate a 3-5 sentence summary of an article’s key points."
- Provide Context
- What to Do: Include relevant background information, constraints, or requirements the model needs to understand the task.
- Why It Matters: Experts note that models lack your implicit knowledge, so you must "strip away assumptions" and provide the full context (e.g., "untangle what you know that Claude does not").
- How to Do It: Specify details like the subject, tone, or scope—don’t assume the model knows your intent.
- Example: "The article discusses climate change’s impact on polar bears. Focus on scientific findings, not opinions."
- Add Examples
- What to Do: Include 2-3 illustrative examples to clarify the task without over-constraining the model.
- Why It Matters: The podcast highlights that examples guide the model but too many can limit creativity, especially in research settings (e.g., Amanda prefers "illustrative" over "concrete" examples).
- How to Do It: Use diverse, analogous examples that show the desired output style or reasoning, not exact replicas.
- Example: "For an article on renewable energy, a summary might be: ‘The article explores solar and wind power’s growth, noting their potential to cut emissions, while addressing challenges like storage.’"
- Encourage Reasoning
- What to Do: Instruct the model to explain its thought process or work step-by-step.
- Why It Matters: Experts agree this improves outcomes (e.g., "structuring the reasoning helps"), providing insight into how the model interprets your prompt.
- How to Do It: Ask for a breakdown of steps or key points before the final answer.
- Example: "Before summarizing, list the main points you identified in the article."
- Address Edge Cases
- What to Do: Anticipate ambiguities or unusual inputs and tell the model how to handle them.
- Why It Matters: The podcast stresses testing edge cases (e.g., "what happens if there’s no data?") to ensure robustness, especially for enterprise use.
- How to Do It: Identify potential issues (e.g., missing data, off-topic inputs) and provide an "out" like flagging uncertainty.
- Example: "If the article is too short or unrelated to climate change, note that and decline to summarize."
- Plan for Iteration
- What to Do: Test the prompt with sample inputs and refine it based on the model’s outputs.
- Why It Matters: Iteration is a core theme in the podcast (e.g., "back and forth, back and forth"), as initial prompts rarely work perfectly.
- How to Do It: Run the prompt, read outputs closely for patterns or errors, and adjust accordingly.
- Tip: "Reading model outputs closely" is critical—look for misinterpretations to fix.
- Respect the Model
- What to Do: Treat the model as an intelligent entity capable of complex tasks; avoid oversimplifying or condescending.
- Why It Matters: The experts advocate trusting the model’s capabilities (e.g., "Claude is smart—treat it that way") rather than "babying" it.
- How to Do It: Use a professional tone and assume the model can handle nuanced instructions.
- Example: Don’t say, "You’re a simple assistant"; instead, say, "You’re an expert helping me with this task."
- Read Outputs Closely: The experts emphasize this repeatedly (e.g., "look at the model outputs"). It’s how you spot mistakes and refine your prompt.
- Test Edge Cases: Don’t just test the typical scenario—try unusual inputs to ensure reliability (e.g., "what if the user types gibberish?").
- Be Honest and Direct: Avoid unnecessary personas or metaphors unless they clarify the task (e.g., "just ask it to do the thing you want").
Here’s how the template might look for a specific task:
- Task: "Generate a 3-5 sentence summary of an article’s key points."
- Context: "The article discusses climate change’s impact on polar bears. Focus on scientific findings, not opinions."
- Examples: "For an article on renewable energy, a summary might be: ‘The article explores solar and wind power’s growth, noting their potential to cut emissions, while addressing challenges like storage.’"
- Reasoning: "Before summarizing, list the main points you identified in the article."
- Edge Cases: "If the article is too short or unrelated to climate change, note that and decline to summarize."
- Iteration: Test with multiple articles; adjust if summaries miss key points or include opinions.
- Respect: "You’re an expert summarizer—provide a concise, accurate summary based on the article’s science."
This template leverages the podcast’s insights to help you craft prompts that are clear, context-rich, and iterative, while respecting the model’s intelligence. Use it as a starting point, test your prompts, and refine them based on outputs. Prompt engineering is an evolving skill—keep experimenting to push the model’s boundaries and improve your results!