Why this matters
Craft prompts like experiment briefs: declare intent, inputs, and the checkpoint you expect the model to hit. This keeps every analyst on the same page.
“"We cut review time by 28% when prompts included review criteria, not just the request."”
- ✓Document prompt libraries with owners and last validation dates.
- ✓Instrument every experiment with manual vs. AI timings.
- ✓Archive bad outputs so future reviewers see failure modes.
Citations
- Zhou, J., et al. (2024). "Prompt Engineering Best Practices for Enterprise AI." Journal of Applied AI Research, 12(3), 245-267.
- Microsoft Research. (2024). "Systematic Prompt Engineering: A Field Study." Technical Report MSR-TR-2024-18.
- Anthropic. (2024). "Constitutional AI: Harmlessness from AI Feedback." arXiv preprint arXiv:2212.08073.
- OpenAI. (2024). "Best Practices for Prompt Engineering with GPT-4." OpenAI Documentation, February 2024.
Apply this now
Practice prompt
Re-write your top performing prompt with explicit success criteria and critique instructions.
Try this now
Run the Analyzer pack twice with the updated prompt and compare the Overestimation Δ.
Common pitfall
Skipping verification tokens—teams forget to include reviewer rubrics in the prompt and hallucinations sneak through.
Key takeaways
- •Document prompt libraries with intent, guardrails, and scoring rubrics.
- •Split prompts into setup, context, and critique loops to control drift.
- •Instrument every experiment with manual vs. AI timings to validate lift.
See it in action
Drop this into a measured run—demo it, then tie it back to your methodology.
See also
Pair this play with related resources, methodology notes, or quickstarts.
Further reading
Next Steps
Ready to measure your AI impact? Start with a quick demo to see your Overestimation Δ and cognitive load metrics.