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A short code tour helps an AI coding agent find the right files in three moves: it narrows the task to one behavior, gives the agent a small map of likely entry points and modules, and then has the agent follow one concrete input through the code. The tour does not make the agent’s answer correct. What it does is make the agent’s path through the repository visible, so you can check each named file, symbol, and test before relying on the explanation.
Start with one behavior, not the whole repository
Asking an agent to explain an entire codebase produces a broad summary that is hard to verify. A bounded question, such as where an API response is assembled or how a form saves its data, gives the agent a target and gives you a way to judge whether the explanation is complete. If you know the starting route, command, or UI element, name it. That detail alone often removes a lot of aimless searching.
Give the agent a small map and ask why each file belongs
Microsoft’s VS Code guide, “Explore a codebase with an agent,” recommends asking for a short map with four parts: the entry point, the implementation modules, the relevant configuration, and the relevant tests. The important part is the instruction to say why each file is in the investigation. A file named with a one-line role is easy to check. A file named without a reason is a guess you have to rebuild yourself.
Trace one concrete input to its result
A map shows where code lives; a trace shows how it is used. Pick one realistic request or input and ask the agent to follow it through the implementation. A useful trace records:
- the inputs at each step and the values they carry;
- the call sites that connect one step to the next;
- the return paths, including what the caller receives;
- error cases and the points where the code crosses into an external service or boundary.
Traces expose gaps quickly. If the agent cannot name the caller that connects two steps, the explanation has a hole at exactly that point.
Check every reference before you trust it
GitHub’s Copilot guide, “Using GitHub Copilot to explore a codebase,” and the VS Code guide both treat the agent’s explanation as something to verify against the code. Ask for file and symbol references, then open each referenced file yourself. Confirm three things:
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- the code is active in the relevant application, not a dead module, a copy, or a sample;
- the cited callers actually connect the steps the explanation describes;
- the implementation is current, meaning it matches the version you are working on.
Tests need a separate check. Distinguish tests you inspected from tests you actually ran. An agent can describe what a test asserts without having run it, so do not write that a test passes unless you ran it and saw it pass.
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Keep the repository map short and point it at deeper docs
OpenAI’s engineering account, “Harness engineering: leveraging Codex in an agent-first world,” describes using a short AGENTS.md as a table of contents that points to a structured documentation directory. The idea is progressive disclosure: the agent starts with a small, stable entry point and follows pointers to deeper material only when a task needs it.
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OpenAI reports the opposite pattern as a failure. An oversized instruction file consumes scarce context, can cause agents to miss constraints, and becomes hard to keep fresh and verify. These are lessons from OpenAI’s own engineering work, not a controlled comparison of documentation designs, so treat them as strong guidance rather than settled measurement.
Microsoft’s ShadowFrog project illustrates the file-backed version of this idea. It describes a shadow directory of Markdown organized by symbol, with references to source paths and file symbols. The project describes itself as a research project, so it shows what the category looks like rather than proving that such systems improve results.
Give the agent the right context
GitHub’s guide describes two ways to supply repository context. You can attach a repository to a chat, or ask a question from the repository page. You can also narrow context to a directory, a file, or a symbol. The guide says natural-language questions asked in a repository context work best when the semantic code search index is up to date, so check that indexing is current before asking a broad question. The repository-page flow is marked as public preview and subject to change, so confirm the current interface in your product before building a team process around it.
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A 2026 study on arXiv, “How Developers Experience Debugging Unfamiliar Codebases with Code Tours Generated and Evaluated by Local LLMs,” is a qualitative study, and its findings are about preferences and perceptions rather than measured productivity. The authors report that participants generally favored tours that:
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- scaled their detail to the length of the code;
- were easy to scan;
- explained behavior rather than restating the code;
- used a guiding tone.
The same study reports that developers trusted descriptions they perceived as human-written more than those they believed were AI-generated. It also reports that LLM-generated annotations rating tour quality were unreliable. Those results argue for keeping a human review step, and for recording which parts of a tour you have verified.
Practically, keep three lists as you go: verified facts (you opened the file and saw the behavior), assumptions (plausible but not yet checked), and open questions. Only promote stable knowledge into shared documentation after someone reviews it against current code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A repeatable tour, step by step
- State one behavior in a single sentence, and name the starting route, command, or UI element if you know it.
- Ask for the entry point, implementation modules, relevant configuration, and relevant tests, with a one-line reason for each file.
- Choose one realistic input and ask the agent to trace it through the implementation, including call sites, return paths, and error or external-service boundaries.
- Request file and symbol references for every claim.
- Open each referenced file and confirm it is active, current, and connected as described.
- Compare the explanation with the tests, noting which you inspected and which you actually ran.
- Sort the results into verified facts, assumptions, and open questions before anyone relies on them.
Comparing orientation approaches
The following comparison uses decision axes drawn from the official workflow and engineering guidance above. It is a way to choose an approach, not a formal vendor comparison.
| Axis | Short tour for one behavior | Broad repository summary | Monolithic instruction file |
|---|---|---|---|
| Scope | One task or behavior, traced end to end | Whole repository at once | Every rule and convention in one document |
| Structure | Compact map pointing to deeper source material | Free-form overview | Single large file that the agent must read in full |
| Evidence | File and symbol references, traced call paths, tests inspected or run | Not stated in the sources reviewed | Depends on whether the document cites code |
| Maintenance | Map kept short, checked against current code | Not stated in the sources reviewed | OpenAI reports these files become stale and hard to verify |
| Context access | Repository attachment, directory, file, or symbol context with a current semantic index | Not stated in the sources reviewed | Not stated in the sources reviewed |
Starter questions that work well
GitHub’s guide offers two example prompts that are bounded enough to verify:
- “Where is authentication handled in this codebase?”
- “What are the main entry points and how do the key components fit together?”
The first is a good first tour because it has a clear endpoint: you can follow authentication from the entry point to the check that accepts or rejects a request. The second is broader, so narrow it to one entry point before tracing it.
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