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AI coding assistants can use code from your project when a tool supplies or retrieves it, but that does not mean they see or understand your entire, up-to-date repository on every request. What informs an answer depends on the product, feature, files and permissions available, context limits, indexing and privacy settings. Repository context can make an answer more relevant; it cannot guarantee that the answer or generated code is correct.
What “codebase-aware” actually means
Codebase awareness is a way a tool gathers context, not a promise of complete knowledge. Depending on the product and feature, an assistant may use the active file or selected code, open files, workspace details, conversation history, a repository index, search results, or files it reads for a particular task. The context can differ between an editor chat, a hosted-repository workflow, and an agent that can inspect files.
Even when a repository is indexed, the assistant generally retrieves material relevant to the current question rather than putting every file into one prompt. GitHub describes semantic search for locating relevant repository sections; Cursor documents context limits that vary by model; and Anthropic describes compacting earlier Claude Code conversation to make room. These mechanisms mean the choice and capacity of context can affect a particular answer. They do not establish a universal percentage of any repository that an assistant “knows.”
How selected assistants get code context
These documented workflows illustrate why the name of a product alone does not tell you exactly what was considered. Features, settings, plans, and product surfaces matter.
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| Assistant | Documented context path | What to check |
|---|---|---|
| GitHub Copilot | With repository context, Copilot Chat can index a repository and use semantic code search to find relevant code. GitHub says initial indexing for a large repository can take up to 60 seconds and is typically updated automatically when a new conversation starts. Prompt context can also include the current repository, open files, chat history, active file, selection, and workspace details; supported GitHub.com workflows may retrieve repository data or web search results. | Confirm that repository indexing is available and enabled for your workflow, that the index covers the material you need, and that any exclusions or workspace limitations are understood. For non-GitHub workspaces in VS Code, semantic indexing uploads data to GitHub; enterprise policy must enable it. GitHub’s repository indexing documentation and its Copilot feature page describe context paths. |
| Cursor | Cursor documents workflows for understanding, planning, building, debugging, and reviewing code. Its AI features send prompts and code context to model providers such as OpenAI, Anthropic, and Google. | Check the selected model and its context capacity, what code is being supplied, and the active privacy configuration. Cursor’s documentation describes product workflows; its privacy and data documentation describes data handling. |
| Claude Code | Anthropic says Claude Code runs on the user’s machine, reads source files locally, and sends only the portions needed for the current task to the API. The FAQ says /compact summarizes earlier conversation to free context and /clear starts fresh while retaining project instructions and settings. |
Distinguish local file access from what is sent to the API, and account for conversation context management. These statements apply to Claude Code, not to cloud-indexed tools generally. See Anthropic’s Claude Code user FAQ. |
Why an assistant can miss something or get it wrong
- It may not have the relevant file. A file can be outside the active context, unavailable to the feature, excluded, or not retrieved for the question.
- Its view can be stale or partial. An index may take time to build or update, and the material retrieved for one prompt need not include recent changes elsewhere.
- Context has limits. Model capacity and product strategies for selecting or compacting context constrain what can inform a response.
- Understanding is not verification. GitHub notes limitations with complex code structures and less common languages. Even a well-grounded explanation can overlook an edge case, dependency, or security issue.
GitHub recommends secure coding practices and review of generated code in its responsible-use guidance. Treat an answer as a reasoned suggestion based on available context, not proof that the tool inspected every dependency or validated the whole system.
Separate file access from data handling
“Can it read my code?” and “Where does my code go?” are different questions. A tool may read files locally, index or process repository content on a hosted service, or send selected prompts and code context to a model provider. Whether submitted data is retained or used for training is another question. One answer does not settle the others, and a policy for one vendor should not be generalized to another.
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GitHub Copilot
GitHub says Business and Enterprise customer data is not used by GitHub to train AI models. For individual plans, GitHub may use interaction data subject to applicable settings and privacy terms, and users can opt out. The applicable account, settings, and terms matter; see GitHub’s model-hosting documentation.
Cursor
Cursor says its AI features send prompts and code context to model providers. Its Privacy Mode documentation says code is not used for training when that mode is enabled, while noting exceptions for requests made with your own API keys and certain models outside zero-data-retention agreements. Verify the configuration, account, provider, and current terms in Cursor’s privacy documentation.
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Claude Code
Anthropic’s statement that Claude Code reads source files locally and sends task-needed portions to its API describes that product’s workflow; it does not establish how other assistants handle code. Consult the Claude Code FAQ for its stated behavior.
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How to check what informed an answer
- Identify the exact feature and surface. Note whether you are using editor chat, repository chat, semantic search, or an agent, and which model or provider is selected.
- Inspect the context. Check active files, selected code, open files, retrieved source references, and any repository context the product exposes. Ask the assistant to identify the files or evidence behind a claim when the interface supports it.
- Check coverage and freshness. Look at indexing status, exclusions, permissions, and whether recent changes are available to the current session. Do not assume a successful answer proves all relevant files were searched.
- Review privacy controls and policy. Confirm the account plan, privacy settings, provider, retention and training terms, and any organization-enforced controls before using sensitive or regulated code.
- Verify the result in your normal workflow. Inspect generated changes, review security implications, and run the project’s relevant tests and checks before accepting a patch.
Safer ways to work with repository context
- Do not put secrets in prompts or source files supplied to an assistant.
- Use available exclusions, permissions, and organizational controls to limit access to appropriate code.
- For a consequential explanation, ask for relevant file paths or source references and inspect them yourself.
- Review every generated change and test it under the same standards as code written by a person.
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