Analysis
This article highlights an exciting shift in how we approach knowledge retrieval for Large Language Models. The Claude Code developer's move to Agentic Search, bypassing traditional Retrieval-Augmented Generation, opens the door to innovative possibilities. This could lead to more efficient and dynamic information access.
Key Takeaways
- •The Claude Code developer replaced RAG with Agentic Search, a method where the model uses grep/glob for information retrieval.
- •Agentic Search allows the model to autonomously search files, potentially reducing reliance on pre-indexed data.
- •This shift suggests that vector search, commonly used in RAG, might be a transitional technology in LLM development.
Reference / Citation
View Original"Claude Code's developer Boris Cherny (@bcherny) tried everything: Voyage embeddings, vector databases, and semantic search. Ultimately, he concluded that Agentic Search was overwhelmingly better."