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Why AI coding assistants change code style
An assistant generates or edits code using a prompt and the repository context its tool makes available. If local conventions are not stated or included, the assistant may produce code that reflects its general defaults rather than the project’s preferences. Conflicting or overly broad instructions can also make the intended style harder to follow. OpenAI’s Model Spec describes defaults as a way to make behavior more predictable while allowing them to adapt to developer and user needs.
“Style” can mean more than whitespace. It may include line wrapping, naming, imports, error handling, file placement, or architectural patterns. Formatters primarily standardize code presentation; naming and design choices usually need project instructions or other checks.
There is no universal instruction filename. Microsoft’s Visual Studio Code guide describes different harness conventions, including .github/copilot-instructions.md for Copilot, CLAUDE.md for Claude, and AGENTS.md for Codex. Confirm the discovery rules for the assistant you actually use. A file that one tool reads may not be read by another.
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How to make an assistant follow your project’s conventions
- Write concise, project-specific guidance. Record decisions the assistant cannot reliably infer from the code alone: naming rules, relevant architecture, the formatter command, and what counts as a finished change. Microsoft’s guide identifies these kinds of project decisions as useful instruction material.
- Use the instruction file supported by the active harness. Check its documentation and scope. If a team uses multiple assistants, keep their guidance consistent without assuming they share a discovery format.
- Check discovery, then check behavior. If the tool shows which instruction files it has loaded, confirm the right one appears. Then give it a representative task and compare the result with a baseline. Discovery shows the file was found, not that every instruction was followed.
- Run the repository’s formatter. Use the project’s documented command rather than relying on the assistant to decide to format. Anthropic’s guidance on steering Claude Code distinguishes a model choosing to invoke a formatter from a hook invoking it automatically.
- Run tests and other checks separately. A cleanly formatted diff can still contain a bug. Treat formatting, linting, tests, and the project’s definition of done as separate checks.
Keep instructions accurate and avoid duplicating rules across files without a reason. Anthropic notes that long or ambiguous guidance can be difficult for a model to apply and that additional instructions can have diminishing returns, particularly when they conflict.
What formatters do—and what they do not do
A formatter applies defined rules to code presentation, making the result more consistent and repeatable than relying on an assistant’s stylistic choices. It does not establish that a change is logically correct, secure, or consistent with every architectural preference.
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Tools serve different roles. Prettier describes itself as an opinionated code formatter. Black formats Python code. EditorConfig helps maintain shared editor settings; it is not a substitute for a language formatter. A repository may use complementary tools.
When selecting or reviewing a setup, check which languages and files it covers, how much control it offers over output, whether its rules match existing code, and whether it can run in the editor or CI. The key operational question is whether formatting runs automatically when needed or depends on someone remembering to invoke it.
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Should you use instructions or a formatter?
Use both when you want AI-assisted changes to fit a project reliably. Instructions communicate project-specific decisions and tell the assistant which command or conventions matter. A formatter applies supported presentation rules consistently after generation or editing. If style requirements include naming, file placement, or design patterns, document or validate those separately; a formatter may not cover them.
For reliable enforcement, teams can configure an editor action, hook, or CI step to run the formatter at the appropriate point. The exact mechanism depends on the assistant and repository. Keep automated formatting distinct from tests: formatting normalizes presentation, while tests and other checks evaluate different aspects of the change.
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What the evidence can and cannot establish
The cited product documentation explains instruction discovery, formatter roles, and automation options, but it does not quantify how often assistants drift from project style or how much formatting automation reduces that drift. There is no basis here for a percentage or a cross-assistant ranking. A team can assess its own setup by repeating a representative task with the same harness and comparing the outputs against its conventions.
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