Should I ask the AI to explain the code or just generate it?

Ask for the code first, then request an explanation only for the parts you do not understand. Bundling a long explanation into every request wastes tokens and buries the code you actually need to copy. For learning, a follow-up "explain line 12" is cheaper and sharper.

Why does the AI keep using libraries I did not ask for?

Because you did not constrain it. Always state "standard library only" or list the exact dependencies allowed. Models default to popular packages, which is fine for prototypes but breaks builds in locked-down environments. Constraints in the prompt prevent the cleanup later.

How do I get the AI to fix a bug instead of rewriting everything?

Give it the exact error message, the input that triggers it, and the expected output, then say "change as little as possible." Without "minimal change" instructions, models tend to rewrite working code and introduce new regressions. Pin the scope explicitly.

Is it better to use a chat model or a dedicated coding tool?

For a single function or snippet, a chat model with a tight prompt is fastest. For multi-file changes across a real repository, a coding agent that can read your files and run tests is far more reliable because it has the context a chat window lacks. Match the tool to the blast radius.

How long should a coding prompt be?

As long as it needs to carry the goal, the constraints, and the relevant existing code — and not one word longer. A precise three-line prompt beats a vague three-paragraph one. The skill is choosing which context matters, not writing more text.

How to Write Prompts for Coding Tasks

Why coding prompts are different

A prompt for an essay can be loose because there are many acceptable answers. Code has to compile, pass tests, and match an existing codebase, so the prompt has to be precise. The model has no access to your repository, your runtime, or the error you are staring at — it only knows what you put in the prompt window. Most “the AI gave me bad code” complaints are really “I gave the AI bad context” complaints. Get the inputs right and the output improves dramatically.

The builder below assembles a clean request from the parts that actually matter: the task, the language and version, the constraints, the existing code, and whether you want tests. Fill it in, copy the result, and paste it into any LLM.

How it works

Strong coding prompts share a structure. First, the goal — one plain sentence: “Write a function that deduplicates a list of records by email, keeping the most recent.” Second, the environment — language, version, and allowed dependencies, because the model will otherwise reach for whatever is popular. Third, the constraints — performance limits, style rules, error handling expectations. Fourth, the existing code — paste the type definitions and surrounding functions so the new code fits. Finally, the verification — ask for tests, or a usage example, so you can check the result without trusting it blindly.

The biggest leverage is the feedback loop. Generate, run, and when something fails, paste the exact error and the input that caused it back into the chat. Say “change as little as possible to fix this.” Models are excellent at the narrow fix and dangerous at the speculative rewrite, so keep the scope pinned.

Patterns and tips

Pin the version. “Python 3.11” or “Node 20, ESM, TypeScript strict” stops the model from using syntax your runtime rejects.
Forbid surprise dependencies. Add “standard library only” or list the exact packages allowed. This alone removes most build failures.
Ask for tests first for tricky logic. “Write failing tests for these edge cases, then the implementation that passes them” produces more correct code than asking for the implementation alone.
Give it the error verbatim. Stack traces contain the answer; do not paraphrase them. Include the line that threw and the input value.
Use “minimal change.” When fixing, explicitly forbid refactoring working code so you do not trade one bug for two.
Request an explanation only on demand. Generate code, then ask “explain the regex on line 4” rather than asking for a tutorial every time.