Making AI coding assistants better, even for your weird DSLs - George Fahmy
AI Summary
Summary of the Video
- Presenter Background
- George, founder of Stackpack.
- Focus on improving developer experience in coding assistants for domain-specific languages (DSLs).
- Challenges with LLMs and DSLs
- LLMs excel in writing actual code but struggle with DSLs like SQL, Terraform, and Kubernetes due to their complexity and variability.
- Performance drops significantly with unfamiliar or less popular DSLs due to limited training data.
- Rapid updates in DSLs (e.g., Kubernetes) further complicate LLM performance.
- Techniques to Improve LLM Performance with DSLs
- Bounded Decoding: Ensures valid code generation by using finite automata based on regex, leading to 100% valid outputs.
- Retriever-Augmented Generation (RAG): Enhances performance by using contextual information from user projects and documentation, with a focus on query expansion and proper sourcing from documentation rather than just code samples.
- Grammar Prompting: Teaches LLM the rules of DSLs using BNF format, improving accuracy, especially for new DSLs.
- Recovery and Retry: Incorporates static analysis to validate generated code and provide feedback for corrections.
- Implementation Insights
- Combining these techniques can lead to significant performance enhancements with minimal resources, showing that smaller models can still be effective with the right structures in place.
- Importance of continuous feedback and adaptation for LLMs to handle the dynamic nature of DSLs.