The Essence of Prompt Engineering Is the Art of Asking Questions (ramsayleung.github.io)

The piece reframes prompt engineering not as a set of arcane tricks but as the modern application of Eric S. Raymond’s “How To Ask Questions The Smart Way”: effective AI interaction is fundamentally the art of asking the right questions. Rather than sharing clever prompts, the author urges practitioners to do homework first (search docs/forums, reproduce the issue), present observable symptoms (logs, runtime metrics, screenshots) instead of guesses, and state goals rather than lock into a specific method (e.g., “merge two tables” vs “how to use VLOOKUP”). Concrete examples—showing a 403 scraping error with headers tried, or memory growth from 100MB to 2GB—illustrate how evidence-rich prompts let models and collaborators pinpoint issues faster. For AI/ML practitioners this matters because it reduces hallucinations, improves controllability, and turns models into collaborators instead of oracles. Practically, use minimal reproducible examples, specify input/expected/actual outputs, and follow a phased workflow: ask for an approach, request 2–3 feasible solutions with trade-offs (performance, maintainability, complexity), confirm a direction, then request detailed implementation. Finally, structured politeness and closure—reporting what worked—creates a feedback loop that mimics human-in-the-loop learning and yields better long-term outcomes. In short: mastering prompt engineering is mastering how to communicate problems with precision and purpose.