Prompt Engineering with AI

Models are very good at writing and improving prompts — they’re a helpful first pass, not the final word. Treat the model like a fast collaborator: give it enough context to understand the task, let it propose small changes, then you decide what sticks.

Checklist and workflow:

• Start with 1–2 AI revisions as a first pass, then evaluate on real examples; you decide what sticks.
• Provide real context and concrete feedback; avoid generic requests like “Improve the prompt” or “Reduce hallucinations.”
• Include 1–3 representative I/O examples with brief notes to guide meaningful edits.
• Ask for targeted edits on exact passages; keep changes small and focused.
• Use eval tooling when useful (e.g., LLM‑as‑a‑judge), but don’t overfit—prefer minimal, generalizable changes.
• Distill many feedback items into concise guidance before edits if needed; evaluate changes on real data, since automated evals can be wrong.
• If AI revisions don’t help, perform manual edits to set direction, then bring the model back for fine‑tuning.
• Avoid more than two consecutive AI‑only edits to prevent drift and overfitting.
• Expect prompts to grow; prioritize clarity and useful context first, then condense when length truly matters.

When you follow this approach, model‑assisted prompt editing becomes a force‑multiplier!