Claude Codebase Research: Map the Code Before Editing

Asking Claude to research a codebase means asking it to read, trace, and summarize the structure before writing a single line of a change. That sequence — understand first, edit second — reduces the chance of a modification that looks right in isolation but breaks something three layers away.

The problem is that a research session produces findings that disappear when the chat closes. Tools like fenbs keep the verified code map, open questions, and remaining checks outside the chat so the next session — or a colleague using a different AI app — starts from the same confirmed understanding.

Start with entry points, not the whole codebase

A large repository is too much for one session. Start with the smallest useful slice: the file that handles the request you want to change, the module that exports the function you need to modify, or the route that triggers the behavior in question.

Give Claude a specific question to answer rather than a general instruction to explore. "Where does this API endpoint validate the incoming payload?" produces a focused answer. "Explore the repository" produces a long summary that buries the detail you actually need.

How to trace behavior across files

1. Identify the entry point. For a web request, that is the route handler. For a background job, that is the scheduler or queue consumer. For a UI action, that is the event handler.
2. Ask Claude to read that file and describe what it does: what it receives, what it calls, and what it returns or emits.
3. Follow the call chain one step at a time. Ask Claude to read the next file or function it identified and repeat the description.
4. Stop at the boundary that matters for your change. You do not need to trace every path — only the one your change will touch.
5. Ask Claude to summarize the assumptions the code makes about its inputs and the effects its outputs have elsewhere.
6. Write down that summary before ending the session. It is the starting point for the edit session and for any review later.

Verify findings before you trust them

Claude reads code accurately but does not run it. A function Claude describes as the sole validator might have a sibling in a middleware layer it has not seen. A constant Claude reads as fixed might be overridden by an environment variable.

Before treating a finding as confirmed, ask Claude to check it a second way: search for other callers, check for overrides, or verify that the file it read is the one actually loaded in production. Three minutes of checking prevents an edit built on a wrong assumption.

• Ask Claude to search for every call site of the function, not just the one you found first.
• Ask whether the behavior changes based on an environment variable or a feature flag.
• Ask Claude to identify what the code does not do — what error cases it leaves unhandled.
• If the codebase has tests for this path, ask Claude to read them. Tests often document the intended behavior more clearly than the code does.

Common traps when researching code with an AI

A few patterns produce unreliable research results and are worth guarding against.

• Asking for a high-level summary first. High-level summaries miss the specific detail you need for a safe change. Start specific and zoom out only if needed.
• Trusting a file path without checking it exists. Claude may hallucinate a plausible file path if it has not read the directory structure. Verify the path first.
• Stopping research when a likely answer appears. A likely answer is not a confirmed one. Ask Claude to verify it before building on it.
• Carrying forward incorrect findings. If a finding turns out to be wrong during the edit, update your notes. A wrong finding that persists as a recorded "fact" will mislead the next session.

What to record before the edit session begins

A research session is only useful if its results survive the session. Record the confirmed findings in a form another person or AI can act on: what the code does, what assumptions it makes, what it does not handle, and what the next step is.

Keep open questions separate from confirmed facts. "The validator rejects empty strings" is confirmed. "The validator also rejects null values — needs a check" is still open. Mixing them hides the risk.

How to do this with fenbs

1. Create a free account and add a project for the codebase or the work stream that includes this investigation.
2. Add a task for the research work. Write what you are investigating, where in the codebase, and what question you need answered.
3. As findings are confirmed, add them as context notes linked to the task. Keep unconfirmed findings in the task's plan under an "open questions" note.
4. Connect Claude to fenbs at /integrations. At the start of each session, ask Claude to read the task context before reading any code.
5. Before the edit session begins, review the confirmed findings in fenbs and check that nothing has changed in the codebase since the last research session.

For background on what Claude can and cannot do with code, see what is claude code and the claude api post for programmatic access options.