AI coding assistants have become part of the daily toolkit, and the gap between developers who get great results and those who get frustrating ones is rarely about the model — it is about how they ask. Prompting well is not incantation or secret phrases; it is applied communication and specification, the same skills that make a good bug report or ticket. Treat it like engineering and it becomes reliably useful.
The core insight: the assistant only knows what you tell it and what it can infer, so most bad output traces back to an underspecified request.
Give context and constraints
A prompt like "write a function to sort users" leaves everything to guesswork — which language, sorted by what, what a user looks like, what edge cases matter. A good prompt supplies the context: the language and version, the relevant data shape, the constraints ("must be stable", "handle empty input", "no external libraries"), and the surrounding conventions. The more the assistant knows about your actual situation, the closer the first answer lands.
Constraints are especially powerful because they narrow the space of acceptable answers. Telling the assistant what not to do — "do not use recursion", "match the existing error-handling style" — often improves output more than adding another instruction about what to do.
Show, iterate, and decompose
Examples communicate faster than descriptions. Showing a sample input and expected output, or a snippet of your existing code to match, conveys intent that paragraphs of prose would fumble. And treat the interaction as iterative: the first answer is a draft to refine, not a verdict. "Good, now handle the null case" and "that's too clever, make it more readable" steer it quickly toward what you want.
For anything substantial, decompose. Asking for a whole feature in one shot invites sprawling, hard-to-verify output; asking for one function, reviewing it, then the next builds something you actually understand. The assistant is a fast pair, not an autonomous contractor.
The fluency trap
The single most important habit is scepticism about confident wrongness. AI assistants produce fluent, authoritative-sounding code that can be subtly or completely wrong — a plausible API that does not exist, an off-by-one error, a security hole delivered with total confidence. Fluency is not correctness, and the calm, well-formatted tone is not evidence.
So review AI-written code as you would a stranger's pull request, or more carefully: read it, understand it, test it, and never ship what you cannot explain. Used this way — specific prompts, iterative refinement, decomposed tasks, and rigorous review — an AI assistant is a genuine multiplier. Used as an oracle whose output you paste unread, it is a fast way to ship bugs you do not understand.