Kuba Rogut

This talk challenges the notion that Retrieval Augmented Generation (RAG) is obsolete, arguing instead that hybrid, tool-rich retrieval is becoming the standard for sophisticated agentic search. While simple RAG, often equated with basic vector search, was effective in early AI development, more advanced applications now leverage agentic search. This approach involves agents iteratively reasoning over context using a suite of tools, including vector search, full-text search, and other filters, to progressively refine their understanding and complete tasks.

This talk explores the effectiveness of semantic code retrieval for AI coding assistants, specifically benchmarking it against traditional grep-based methods on Claude Code. The core thesis is that while grep is simpler and often sufficient, semantic search, powered by vector embeddings, can significantly improve precision and the agent's ability to locate relevant code, especially for complex tasks. This approach offers a form of cached compute, reducing redundant processing and potentially leading to better accuracy and user satisfaction.