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    Step-by-Step Guide: Refactoring a Large Rust Codebase with aider.dev and Custom LLMs

    By   ·  3 minute read

    Refactoring a sprawling Rust codebase can be daunting, especially when you want to ensure code quality, minimize disruption, and control costs. With aider.dev, developers and DevOps engineers can pair program with advanced LLMs—Anthropic’s Claude, OpenAI, or even local models via LM Studio—to streamline and automate much of this process. This guide will walk you through using aider’s architect and code modes, model selection, and prompt caching to efficiently refactor your Rust project.

    aider-image

    1. Understanding Aider’s Architecture: Architect and Editor Models

    Aider separates code reasoning from code editing using two distinct models:

    • Architect Model: This model analyzes your request, plans the solution, and proposes how to approach the coding problem.
    • Editor Model: This model takes the architect’s proposal and translates it into concrete file edits.

    This separation is especially useful for complex refactoring tasks. For example, you can use a reasoning-strong model (like OpenAI’s o1 or Anthropic’s Sonnet) as the architect, and a more editing-focused model as the editor. You can configure these with the --model and --editor-model flags ^1.

    “Certain LLMs aren’t able to propose coding solutions and specify detailed file edits all in one go. For these models, architect mode can produce better results than code mode by pairing them with an editor model that is responsible for generating the file editing instructions.” ^5

    2. Setting Up Aider with Your LLM

    Aider supports both cloud and local LLMs. To use aider with a local model (e.g., via LM Studio) or a cloud model (e.g., Anthropic Claude), provide the relevant API key or endpoint:

    # Install aider
python -m pip install aider-install
aider-install

# Navigate to your Rust project
cd /path/to/your/rust/project

# Example: Using Claude 3.5 Haiku with prompt caching
aider --model haiku --api-key anthropic=<YOUR_KEY> --cache-prompts

# Example: Using a local LLM via LM Studio
aider --model <your_local_model> --api-base-url http://localhost:1234/v1 --cache-prompts

    3. Choosing the Right Mode for Refactoring

    Aider offers three main chat modes:

    • Code Mode (default): Directly edits and refactors files based on your prompts.
    • Architect Mode: Plans and discusses structural changes before editing. Activate with /mode architect.
    • Ask Mode: Acts as a consultant, answering questions about your code without making changes ^3.

    For large-scale refactoring, start in architect mode to discuss and plan the restructuring of your Rust modules. Once the plan is clear, switch to code mode to execute the changes.

    4. Adding and Managing Files for Context

    Aider works best when you explicitly add only the files you want to edit or review. Use these commands:

    • /add <file>: Add files to the chat for editing.
    • /drop <file>: Remove files to free up context space.
    • /read-only <file>: Add files for context only, not editing.

    This ensures the LLM focuses on relevant code, reducing token usage and cost ^2.

    5. Using Prompt Caching to Control Costs

    Prompt caching is a powerful feature that reduces redundant API calls and speeds up coding. Supported by Anthropic (Sonnet, Haiku) and DeepSeek, prompt caching stores:

    • System prompts
    • Read-only files
    • Repository map
    • Editable files in the chat

    Enable it with:

    aider --cache-prompts

    You can also keep the cache warm with:

    aider --cache-keepalive-pings N

    This pings the provider to prevent cache expiration (Anthropic’s cache lasts 5 minutes by default). Note: Caching stats are unavailable when streaming responses, so use --no-stream if you want those details ^4.

    6. Selecting and Switching Models

    Aider lets you switch models on the fly with /model <model>. For cost-effective refactoring, consider:

    • Claude 3.5 Haiku: Fast and affordable for large codebases.
    • Claude 3.5 Sonnet: More capable, higher cost.
    • Local LLMs via LM Studio: No API costs, but may require more setup and tuning ^6.

    You can set model defaults in .aider.conf.yml and manage API keys in a .env file for security and flexibility ^3.

    7. Refactoring Rust Code: A Practical Workflow

    1. Plan the Refactor:
      • Start aider in architect mode.
      • Discuss module boundaries, function extraction, or trait reorganizations.
      • Example:
    /mode architect
Please propose a plan to split `lib.rs` into smaller modules, grouping related functions and traits.
    1. Execute the Refactor:
      • Switch to code mode.
      • Add target files: /add src/lib.rs
      • Request changes:
    Refactor `lib.rs` as planned, moving networking code to `network.rs` and data models to `models.rs`.
    1. Review and Test:
      • Use /diff to review changes.
      • Use /commit to save.
      • Optionally, set up automatic linting and testing to validate edits ^2.
    2. Iterate:
      • For large files, break down refactoring into chunks (target 200–300 lines per session).
      • Use prompt caching to minimize repeated context costs ^6.

    8. Advanced Tips

    • Use .aider.conf.yml for project-specific settings (model, auto-commits, etc.).
    • Store API keys in .env for security.
    • Leverage voice and image input for richer context if needed ^2.

    Conclusion

    Aider.dev, combined with advanced LLMs or local models, provides a robust, cost-effective solution for refactoring large Rust codebases. By leveraging architect and code modes, prompt caching, and careful model selection, you can automate and streamline even the most complex refactoring tasks—while maintaining control over cost and code quality ^1^4^7.

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