Tools: The #1 sentence I add to prompts that makes them better

Prompt Engineering or selling your soul? ## Why it works (and why it’s “vibe engineering”) ## The kind of questions that save you ## A realistic failure mode this prevents ## When I don’t use it ## Copy/paste template you can steal If you use AI at work, you’ve probably had this experience: You write a prompt that feels clear enough, hit enter, and the model confidently produces something that is… technically fine… but not what you meant. Maybe it built the right thing in the wrong style. Maybe it chose an approach you would never ship. Maybe it made assumptions you didn’t realize you were leaving unstated. I’ve started treating prompts like a contract with the devil. Not because I think AI is evil — just because it’s literal, opportunistic, and perfectly willing to sprint in the wrong direction if you give it even a small opening. And you can’t cover every edge case up front. So here’s the one sentence I add to a lot of my prompts that consistently makes the results better: Before you begin, ask any clarifying questions you need to fully understand what I’m asking and to do an excellent job. Most prompting advice is basically: be more specific. That’s true, but it’s incomplete — because the whole problem is that you often don’t realize what you forgot to specify. This sentence flips the dynamic: It turns the model into a reviewer before it becomes an implementer. And it forces the “unknown unknowns” to show up early, while it’s still cheap to correct. My favorite clarifying questions are the ones that expose missing context I didn’t realize mattered. Those questions aren’t just helpful to the AI. They’re helpful to me. Because half the time, I’m using the AI to tease out details I forgot to include in the prompt in the first place. “Write a migration to backfill X safely.” The AI might happily generate a perfectly valid migration that: It’s not “wrong” — it’s just not something you want to discover after you already committed to the approach. If you make the model interview you first, you’ll often get the question you forgot to say out loud: “How large is the table, and can this run on prod without blocking writes?” That one question can save you a very annoying afternoon. I don’t paste this sentence into every single prompt. If I’m asking something small and obvious (“write this one-liner”, “explain this error”, “rename these variables”), it’s unnecessary overhead. But if I’m doing any of these, it comes out almost automatically: Here’s a version you can drop into your own prompts: My default prompt preamble Before you begin, ask any clarifying questions you need to fully understand what I’m asking and to do an excellent job. Optional follow-up (if you want to be extra explicit) It’s not magic. It doesn’t make the model smarter. It just keeps the process collaborative instead of “write a prompt and hope for the best.” Teaser: if you want to hear about the #2 sentence I add to prompts to make them even better, get me to 100 likes 😄 Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - Instead of “I describe something and hope the AI guesses right,” - it becomes collaborative. - “Is there an existing system you want me to use as a template?” - “Is this a large table / high-traffic database?” - “Is this safe to run during business hours?” - “What does success look like: correctness, speed, low risk, or minimal code changes?” - “Do you care about test coverage, or just a working fix?” - locks the table, - runs as one big transaction, - does a full scan, - adds an index in a way that takes forever, - and generally assumes the world is a small quiet sandbox. - writing something new (a real feature, not a snippet) - debugging a complex problem - anything with multiple moving parts - anything where “technically correct” can still waste time - If something is ambiguous, don’t guess — ask. - If there are multiple valid approaches, list the options and tell me what you recommend and why. - After questions are answered, produce the output in a clean, usable format.