Best Practices for Using Gemini CLI Effectively in Production Codebases

The command-line interface for Google Gemini brings large language model capabilities directly into the developer’s terminal. From code generation and test scaffolding to refactoring and documentation, Gemini CLI is designed to accelerate development workflows across Python, C#, TypeScript, and beyond.

But to make the most of this tool in production, it’s not enough to simply ask it to “write code”. Like any intelligent agent, Gemini CLI performs best when given structure, constraints, and clear intentions. And while it’s undeniably powerful, it also makes mistakes—sometimes subtle ones. Fortunately, it also features a sophisticated self-correction mechanism, allowing it to recover from many of its own errors with a little nudge.

Here are the most effective practices for using Gemini CLI, along with tips on how to apply them in real-world projects.

1. Use GEMINI.md as Your AI Project Memory

The cornerstone of productive AI-assisted development is context, and Gemini CLI supports persistent project-level instructions via a special file: GEMINI.md.

Place this file at the root of your repository and include:

• A brief description of the architecture and technologies used
• Coding standards and naming conventions (e.g. “use PEP8”, “prefer PascalCase in C#”)
• Common commands for building, testing, and deployment
• What Gemini must avoid
• Team norms and any non-obvious workflows

Gemini CLI reads GEMINI.md automatically and applies it across all sessions. You can also define .gemini/GEMINI.md globally for shared defaults.

Why it matters: Without this context, Gemini will act on its own assumptions—often leading to inconsistent style or logic. But with it, the assistant begins each task with your expectations in mind.

2. Decompose Tasks with Clear Prompts

Gemini performs best with structured, incremental tasks, not vague requests.

For example, instead of:

“Build me a booking system”

Say:

“Plan the architecture for a room booking app using Next.js and Prisma. Backend in /api, frontend in /web, shared types in /types. Then generate the initial folder structure and README.”

After planning, proceed to:

“Write the API route for creating bookings using Prisma.”

And so on.

Why it matters: Large, ambiguous prompts result in cluttered code and poor file organisation. Gemini is more reliable when it can focus on one concrete subtask at a time.

3. Generate Tests Early and Often (TDD-Style)

A standout use case for Gemini CLI is test generation. It can produce unit and integration tests for existing code, and even help drive test-first development.

For instance:

gemini ask "Write pytest tests for this class" --code booking_service.py

Or go full TDD:

1. Ask Gemini to write a failing test for a missing feature.
2. Run it and confirm failure.
3. Ask Gemini to implement the feature to pass the test.

This loop gives the AI a clear target: make the tests pass. And since Gemini understands shell commands, you can ask it to run the tests with !pytest and react accordingly.

4. Use Shell Integration and File Reading

Within interactive mode (gemini without arguments), the CLI supports !-prefixed shell commands and read-file actions.

Examples:

• !npm test — runs your test suite
• read-file user_controller.ts — loads code into memory for review or editing

This allows Gemini to observe its own work, identify errors, and correct them.

Why it matters: One of Gemini’s strongest features is its self-repair capability. If it introduces a defect or misconfiguration, it can often fix it—provided it has access to the terminal output or log.

A typical pattern:

“Set up the backend project structure” → Gemini creates files
“!npm install && npm run dev” → error
Gemini reads the error, identifies the cause, and fixes it in the next step.

This emergent self-debugging behaviour is powerful—but only works when Gemini sees the consequences of its own actions.

5. Enable Checkpointing for Safe Experiments

Using gemini -c or running with the checkpoint mode enabled, the CLI creates snapshots before every file write or delete.

If the assistant breaks something or oversteps (which it sometimes does), you can immediately roll back using:

/restore

Why it matters: Gemini is useful, but not infallible. Checkpointing creates a safety net so you can explore aggressively, then discard or accept changes selectively.

6. Review Changes Before Applying

Gemini offers an edit command that shows a full diff before making file modifications. This is your opportunity to inspect the change and either approve or reject it.

Best practice:

• Use gemini ask "...edit this file..." only on isolated units
• Review diffs with a critical eye
• Combine with checkpoint mode to compare output easily

Gemini does its best, but can still misinterpret intent, rename something incorrectly, or affect logic unexpectedly.

7. Avoid Global Edits Without Clear Scope

Despite the large context window, Gemini’s understanding is still localised. If you ask it to “rename this field across the codebase”, it might miss occurrences in less obvious places.

Instead, guide it explicitly:

“Rename userId to accountId in UserService.cs, UserDTO.ts, and auth.ts.”

Or:

“First find all usages of userId, then confirm the list with me.”

8. Integrate with CI for Linting and Reviews

You can run Gemini non-interactively for automation tasks, like:

gemini review --staged-files --format checklist

This will scan your current changes and output actionable suggestions, such as missing null checks or questionable naming. It complements linters and static analysis, catching higher-level code smells.

You can also generate docs:

gemini docs --input src --output docs

Warning: in CI, disable code editing unless you’re manually reviewing the output. Use Gemini in “read-only” mode for analysis and feedback.

9. Refactor Incrementally and Explicitly

Gemini handles local refactoring well. For example:

“Break this 80-line function into smaller pure functions and add comments.”

But for architectural rewrites, guide it step-by-step:

• First extract shared logic
• Then define interfaces
• Then migrate modules

Do not expect one command to re-architect a large system. Be the lead; let Gemini handle implementation under your direction.

10. Be Blunt About Non-Negotiables

If you want Gemini to avoid certain actions (e.g. never touch .env), state this clearly in your GEMINI.md or session.

For example:

“DO NOT modify or open .env or terraform.tfstate. Ever.”

Gemini learns this as part of memory and will respect it—unless you forget to remind it. Some developers even add memory rules to enforce this globally.

Summary: Treat Gemini as a Capable Junior, Not an Oracle

Gemini CLI can produce high-quality, production-grade code—but only with direction.

• Give it structure, constraints, and consistent context
• Use GEMINI.md to align on project norms
• Leverage shell and file access to create feedback loops
• Expect occasional mistakes, but use its self-correction features to your advantage

Think of Gemini as a junior teammate who learns quickly and works tirelessly—but still needs mentorship. If used wisely, it will not only boost your productivity, but also help you document, test, and maintain code more reliably across your team.