RAG - Jason Liu
Summary
The content from jxnl.co/writing/category/rag/ discusses Retrieval-Augmented Generation (RAG) systems, highlighting various aspects through a series of speaker sessions and articles. Key themes include the practical application and improvement of RAG, particularly in the context of coding agents and enterprise AI. The site features a "Coding Agents Speaker Series" focusing on economically viable agents like Devin, Sourcegraph's Amp, Cline, and Augment, emphasizing their real-world revenue generation and production use. It also introduces a "RAG Master Series" as a comprehensive guide to RAG systems, covering fundamental concepts, advanced optimization, anti-patterns, and case studies. Specific sessions delve into: * **Domain Experts:** The role of specialized knowledge in vertical AI, with insights from Anterior's Head of Clinical AI. * **Text Chunking:** Technical research on chunking strategies for RAG, presented by ChromaDB. * **Agentic Approaches:** How techniques from coding agents can inform RAG system design, using lessons from Augment. * **Multi-Agent Systems:** A discussion on why single agents with robust context management might be superior to multi-agent systems in certain contexts, as explored by Cognition. * **Benchmarking:** Critiquing standard benchmarks like MTEB and advocating for custom evaluation sets for retrieval systems, with research from Chroma. * **Document Automation:** Achieving high extraction accuracy in document processing using AI, with case studies from Extend. * **Performance Boosts:** Strategies for improving RAG performance, such as fine-tuning re-rankers and embedding models, discussed by LanceDB. * **Custom Embedding Models:** The advantage of building bespoke embedding models for individual customers over generic solutions, as practiced by Glean. The content positions RAG as a foundational technology for AI applications requiring external knowledge and reasoning, distinguishing it from other AI applications due to its blend of information retrieval and language generation complexity. The site also links to related series on "Context Engineering" for technical implementation patterns.