How Gemini CLI builds context and learns about your codebase

When you use Gemini CLI, how does it actually learn about your code? By understanding its internal mechanics, you can provide better context and get more accurate responses from the model.

I dug into the source code of Gemini CLI to find the answer, and discovered a two-part process: a fast, initial snapshot of your project, followed by a deeper, on-demand investigation using its own tools. Here’s how it works.

The initial context snapshot

When Gemini CLI starts, it gathers a high-level snapshot of the local working directory to provide the starting context.

First, it gathers basic environment details, including your current operating system, current working directory, and the current date. Second, it generates a directory structure tree of files and folders. This layout map gives the model an immediate sense of the project's folders and files without reading their actual contents.

The CLI sends this entire snapshot to the model as the very first message in the chat, but as a user of the Gemini CLI, you won't see it. If you want to explore the entire unfiltered conversation history, use the /chat save command and find the JSON file it stores in .gemini/tmp/[SESSION_HASH]/checkpoint.json

On-demand learning with tools

After the initial snapshot, Gemini CLI relies on its tools to investigate the codebase as needed to answer questions or perform tasks. This is the primary way it learns during a conversation. It's analogous to a developer opening files and searching for code as they work.

This searching for relevant files means it might miss important files. I like to start my chat with a few probing questions to make sure it has read the right files, before getting started with the actual task I want to complete.

Another way to influence how the Gemini CLI finds files and explores your codebase is through the instructional context you can add to GEMINI.md. Refer to Context Files in the docs to learn more.

To explore your code, the Gemini CLI has access to several primary tools. The ReadFile and ReadManyFiles tools read the exact contents of individual files so that the model can understand comments, configuration, and business logic. It uses ReadFolder to inspect directory structures, and SearchText to find specific strings or regular expression matches across the codebase. Finally, the Shell tool allows it to execute command-line instructions, enabling it to run test suites or gather live metadata like git status.

You can use the command /tools to list all tools available.

Mentioning files and directories

A recent feature added to the Gemini CLI is the ability to reference files and directories directly in your prompt using the @ symbol. This is an effective way to provide context to the model faster without it having to search for files.

For example, you can ask:

What is the purpose of the @main.py file?

Or, to include a whole directory:

Can you give me a summary of the @src/ directory?

This is now the recommended way to provide file-based context to the model, and the --all-files flag has been removed.

Wrap up

You now know how Gemini CLI learns about your codebase. On startup, it creates a high-level snapshot of your project's directory structure, but doesn't read the file contents. As you chat with it, it actively uses tools to read files, search for code, and run shell commands to learn about the codebase.

Understanding how this works helps you get better responses.