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Why an AI assistant changes a project’s code style
An assistant generates or edits code from a prompt and the repository context its tool provides. If the project’s conventions are unstated, omitted from that context, or ambiguous, the assistant may produce plausible code in a style that differs from the codebase. OpenAI’s Model Spec explains that defaults help make behavior predictable while remaining adaptable to developer and user needs. Defaults can still differ from a particular project’s preferences.
“Style” can mean more than indentation. It may include line wrapping and whitespace, but also naming, imports, error handling, file placement, or architectural patterns. A formatter is suited to consistent syntactic presentation; project instructions and, where appropriate, lint rules are better places to explain broader conventions.
There is no universal instruction filename. Microsoft’s VS Code guide to configuring AI for a codebase describes different discovery formats for different harnesses, including .github/copilot-instructions.md for Copilot, CLAUDE.md for Claude, and AGENTS.md for Codex. Do not assume that one assistant reads another assistant’s file; check the active tool’s supported layout and scope.
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The available product documentation explains plausible causes and recommended controls, but does not establish how often assistants drift from project style across tools or how much a formatter reduces that drift. Treat style consistency as something to configure and verify in your own repository, not as a quantified guarantee.
Make project conventions visible to the assistant
Microsoft’s guide says project instructions are most useful when they document decisions an agent cannot reliably infer from code alone. Include concise, accurate facts that help it make the change correctly, such as the relevant formatter command, naming or architectural conventions, and what counts as a finished change.
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- Use the instruction file and scope supported by the assistant or editor harness actually in use.
- State project-specific rules rather than generic advice the assistant is likely to know already.
- Resolve contradictions and remove stale or duplicated guidance. Anthropic’s Claude Code guidance on steering warns that instructions can fail in long or ambiguous sessions and that adding more rules can have diminishing returns, especially when rules conflict.
- Include the exact formatter command used by the repository instead of relying on the assistant to guess.
Check whether the active tool has discovered the file—for example, by inspecting the references or context it reports, when that feature is available. Discovery is not proof of compliance. For a more meaningful check, compare a representative change made with the guidance against a baseline, using the same assistant and task where practical.
Use a formatter for repeatable presentation
A formatter is a separate tool that applies configured formatting rules to code. Prettier describes itself as an opinionated code formatter, while Black’s documentation covers a formatter for Python. These tools have different language coverage and conventions; choose one that fits the files and rules in the repository. EditorConfig provides shared editor settings and can complement a formatter, but it serves a different role.
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A formatter can normalize supported presentation details such as whitespace or line layout. It does not establish that a change is semantically correct, follows the intended architecture, or passes tests. Formatting, linting, and testing are separate checks; VS Code’s project guidance treats them as distinct commands and parts of a definition of done.
Automate formatting when consistency must be reliable
Asking an assistant to run a formatter may work, but it depends on the assistant choosing and successfully invoking the command. Anthropic distinguishes that model-directed choice from a hook that invokes a tool automatically. If formatting must happen after edits or before changes are accepted, use an editor integration, hook, or CI step appropriate to the repository and assistant setup.
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- Identify the project’s formatter and command. Use the command the repository already documents or configures; do not substitute a formatter with different conventions without considering the impact on existing files.
- Tell the assistant when to use it. Put the command and relevant project conventions in the instruction file the active harness supports.
- Run formatting consistently. Apply it manually for occasional changes, or configure an editor hook or CI check if the workflow requires automatic or merge-time enforcement. The exact mechanism depends on the editor, assistant, and repository.
- Run tests and other checks separately. A cleanly formatted diff can still contain a bug or violate a project requirement.
Choose tools by the problem they solve
| Tool or control | Best suited to | What it does not establish |
|---|---|---|
| Assistant instruction file | Project-specific conventions and workflow details the assistant may not infer reliably; use the active harness’s supported filename and scope. | That the assistant discovered or followed the instructions. |
| Formatter, such as Prettier or Black | Repeatable presentation rules for supported languages and files. Prettier describes itself as opinionated; Black targets Python. | Semantic correctness, architectural fit, or passing tests. |
| EditorConfig | Shared editor settings that can complement project formatting tools. | All formatter behavior or project-specific logic and architecture. |
| Editor hook or CI | Automating a formatting step in a defined workflow. | That the code is correct beyond the checks actually run. |
Before adopting or changing a formatter, check its language and file coverage, how opinionated its output is, configuration options, integration with the project’s editor and CI, and whether it matches existing conventions. Introducing a formatter can create broad diffs if its rules do not match the repository’s current style.
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