HOW TO ASK AI WELL
Not prompt tricks that expire with the next release. Not prompt tricks that expire with the next release. Sourced, dated, and revised when the numbers move.
A model fills every gap you leave with the average of everything it has seen. That single fact explains almost all bad output. Generic in, generic out — not because the tool is weak, but because you asked it to guess and it guessed the most common answer. Everything below is a way of leaving fewer gaps.
Seven habits
Most weak requests describe a topic and hope. Strong ones state the goal, the audience, the length, the thing that must not happen, and what "finished" looks like.
Real examples, actual numbers, your genuine position, the constraint that makes your situation unlike everyone else's. This is the whole difference between output that sounds like the internet and output that sounds like you.
Requesting the steps does two things: it usually improves the answer, and it gives you something inspectable. An answer you cannot check is a claim you have to take on faith.
These systems produce fluent, confident text whether or not the claim underneath is true — and the confidence carries no information about accuracy. Names, dates, figures, citations, legal and medical specifics: check them, every time.
When something is 70% right, most people rewrite the prompt and start over. Faster: say what is wrong with what you got. "Second paragraph is too soft, cut the last sentence, keep the opening."
Before you start, know which part of the work is the part you are for. The judgement, the position, the taste, the responsibility for being right. Hand over the labour, keep the decisions — otherwise you become an editor of things you do not understand.
Other people's private information, credentials, anything under confidentiality, anything you would not want retained. Assume input may be stored or reviewed unless you have a written guarantee otherwise, and check what your specific tool and plan actually promise.
Three ideas worth more than any template
Context beats phrasing. The industry called this "prompt engineering" and the name did damage — it implied secret words. The real skill is supplying the specifics only you have. Context is the craft.
Cheap output raises the value of judgement. When producing a draft costs nothing, the scarce thing becomes knowing which draft is good, and why. That is not a consolation — it is where the work moved. See the silver lining.
The tool has no stake in being right. It will produce a confident answer to a question it cannot answer, because that is what it was trained to do. You are the one who carries the consequence, so you are the one who has to care.
WHY THIS PAGE IS SHORT
This site used to have eleven separate rooms of prompting technique. Most of it was true in 2024 and stale by 2026 — tied to quirks of models that no longer exist. Rather than maintain a museum of expired advice, it is condensed to the part that has not changed: supply context, keep judgement, verify claims. If a technique cannot survive a model release, it was never a skill.
Using AI is not cheating and it is not shameful — the writer does it daily and says so on every relevant page. What matters is disclosure and responsibility: say when a machine did the work, and stay accountable for whether it is true. The people who get hurt in this era are not the ones who used the tools; they are the ones who trusted output nobody checked.