RAG

Retrieval-augmented generation patterns for grounding LLM output in external knowledge.

Give an AI agent control over when, where, and how to retrieve information rather than using a fixed retrieval pipeline.

Ground LLM responses in external knowledge by retrieving relevant documents before generation to reduce hallucinations and stay current.

Answer complex multi-hop questions through iterative cycles of retrieval, reasoning, and gap analysis across multiple sources.

Build trust in RAG outputs through inline citations, out-of-domain detection, and self-correcting retrieval strategies that reduce hallucinations.

Bridge the vocabulary gap between user queries and knowledge base content using hypothetical answers, query expansion, and hybrid search.

Improve retrieval quality by reranking, compressing, and filtering retrieved chunks between the vector search step and LLM generation.

Replace keyword matching with vector embeddings to find documents by meaning rather than exact words, enabling semantic similarity search.