- Learning to prompt is similar to learning to search in a search engine (you have to develop a sense of how and what to search for). A good example from OpenAI.
- Knowing how the style is named makes prompting easier.
- A short prompt is often enough. Larger or more important tasks should include only the parts that matter.
- Goal: Describe the result, not detailed steps unless the process itself matters.
- Context: Add only information or sources that could change the result, and explain what the model should take from each. Too much context can confuse the model.
- Output: Name the audience, format, length, and intended use.
- Boundaries: State what must stay unchanged, what to avoid, and what requires approval.
- A useful coding-agent prompt names the behavior you want, points to the relevant code or reproduction steps, preserves important constraints, and says how to verify the change. Define what “done” means and review outcomes, not lines of code.
- Be more ambitious with models, not just faster.
- Altitude: Ask for the underlying outcome, not just the next task.
- Depth: Use expert methods, raw data, and independent validation—not merely expert-sounding language.
- Range: Bring in methods from other fields, not just simulated personas.
- For difficult, verifiable work, treat the prompt as both an acceptance test and a search policy.
- Define exactly what counts as done.
- List plausible but insufficient results that do not count.
- Diversify before converging: start with independent approaches, keep incompatible routes alive, and share ideas only after each route’s strengths and gaps are clear.
- Turn likely mistakes and edge cases into an adversarial checklist.
- Actively search for counterexamples and have independent reviewers challenge candidate answers.
- Mark a route as blocked when it only moves the problem into an equally hard unproved claim, reopen it only with a materially new mechanism.
- Iterate through
attempt → failure → diagnosis → new approach → draft → audit → repair.
- Require concrete evidence, reject vague optimism, and report the exact remaining gap when nothing passes the acceptance test.
- Designing prompts is an iterative process that requires experimentation.
- Start simple. Review the result and ask for the specific change you want.
- For important work, ask the model to check the result and flag missing information instead of guessing, then review it yourself.
- Learn about the advanced prompting techniques.
- English is becoming the hottest new programming language. Use it.
- Use comments to guide the model to do what you want.
- Describe the problem very clearly and effectively.
- Make the model ask you more questions to refine the ideas.
- If you want to force some “reasoning”, ask something like “is that a good suggestion?” or “propose a variety of suggestions for the problem at hand and their trade-offs”.
- “Prompt engineering” will have a large impact on the usefulness of an agent.
- Sometimes, you should let the models write their own prompts.