Agentic RAG: How the Agent Fixes Bad Retrievals

Standard RAG has no fallback when retrieval returns irrelevant chunks. Agentic RAG adds a grading and reformulation loop so bad results never reach the LLM.

What you just learned:
- Complex queries get broken into focused sub-queries before hitting the vector store
- Each retrieved chunk is scored for relevance by the LLM
- Low-scoring results trigger a query reformulation and a new retrieval attempt
- Only chunks that pass the relevance threshold continue downstream

Agentic RAG improves retrieval accuracy in production pipelines by treating the retriever as one step in a decision loop, not a single-shot lookup. This pattern is especially useful for multi-hop questions where a single query consistently returns off-topic chunks.

#ai #aiagents #retrievalaugmentedgeneration #python #langgraph

Видео Agentic RAG: How the Agent Fixes Bad Retrievals канала ByteBuilder