Agentic RAG vs Traditional RAG: Implementing Multi-Hop Search with o3-mini
Deep dive into how agentic reasoning loops transform static document retrieval into dynamic multi-hop research engines.
The Limitations of Single-Shot Vector Retrieval
Traditional Retrieval-Augmented Generation (RAG) relies on a single cosine-similarity lookup in a vector database. When answering complex questions that require synthesizing data across multiple files, single-shot retrieval frequently fails.
Agentic RAG leverages o3-mini to evaluate intermediate search results and dynamically issue follow-up queries until all evidence is gathered.
Read our full comparison in the RAG vs Fine-Tuning Learn Card.
Frequently Asked Questions
Multi-hop RAG is an iterative search process where the LLM executes sequential queries, using findings from step 1 to inform query step 2.