
Coding Chats Building the Rig AI framework with Rust
Jan 1, 2026
Josh (Joshua Mo), a software engineer and co-maintainer of the Rig AI framework, shares his insights on building AI agents using Rust. He discusses the intricacies of embeddings and context management crucial for effective AI communication. Josh recounts his transition from technical writing to AI, detailing the challenges of coding agents. He emphasizes prompt strategies for better model interaction and explores the benefits of Rust, such as performance and safety. The conversation wraps up with ways to contribute to the Rig community and future directions in AI frameworks.
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Use Vector Stores For Tool Ranking
- Use retrieval-augmented generation to query embeddings by embedding the user query and returning similar results with payloads.
- Store tool definitions in a vector store to perform tool ranking and avoid bloating the model context window.
Keep Prompts And Context Windows Tight
- Keep prompts and system prompts concise to avoid bloating the context window and confusing the model.
- Prefer compact document formats and avoid adding unnecessary tools or documents into the conversation.
Rust Lets You Build AI In One Language
- Using Rust for Rig avoids language switching and lets you type into existing Rust code and services directly.
- Rust offers type safety, memory safety, and performance benefits once you overcome the learning curve.
