rag-implementation

Retrieval-augmented generation (RAG) grounds LLM outputs by retrieving relevant documents from a vector store and passing them as context. Build a RAG system by setting up embeddings, storing vectors in a database, implementing retrieval strategies, and chaining the retriever to your LLM to answer questions with source citations.

Dense retrieval uses vector embeddings for semantic matching; sparse retrieval uses keyword matching; hybrid combines both. Dense retrieval captures semantic meaning, sparse retrieval excels at exact term matching, and hybrid balances precision and recall for more robust document retrieval across diverse query types.

Yes, reranking refines initial retrieval results by scoring candidate documents with a more expensive model to surface the most relevant ones. This technique improves answer quality and reduces noise when retrieving from large knowledge bases by re-evaluating top candidates before passing them to the LLM.

Vector databases store document embeddings generated by embedding models, enabling semantic search. Configure your embedding model, encode documents into vectors, index them in a vector store, and query using the same embedding model to find semantically similar documents without exact keyword matching.

Source citations provide traceability and verifiability for LLM-generated answers by linking responses to the original documents retrieved. This builds user trust, enables fact-checking, and is essential for applications like document Q&A, research tools, and compliance-driven chatbots requiring accountability.

RAG retrieves at query time, making it flexible for current information but adding latency; fine-tuning encodes knowledge into model weights, enabling faster inference but requiring retraining for updates. RAG scales better for large external knowledge bases; fine-tuning requires more computational resources and loses source traceability.