rag-implementation

Retrieval-augmented generation grounds LLM answers in external knowledge sources by retrieving relevant documents and integrating them into the response. This approach reduces hallucinations and provides sourced, accurate answers for knowledge-grounded QA, chatbots, and semantic search applications.

Dense, sparse, and hybrid retrieval strategies each serve different needs. Dense retrieval uses vector similarity, sparse uses keyword matching, and hybrid combines both. Multi-query and HyDE techniques expand retrieval coverage, while reranking improves result quality by re-scoring candidates.

Load documents, convert them to embeddings using an embedding model, store embeddings in a vector database, then retrieve similar documents to answer queries. Integrate retrieved context into LLM prompts to generate grounded, sourced responses for document QA and knowledge access.

Yes. RAG supports semantic search over proprietary documents and domain-specific knowledge bases by storing embeddings in vector databases and retrieving relevant sources based on query similarity, enabling accurate, contextual answers with source citations.

Reranking re-scores retrieved documents to improve relevance and move the most accurate results to the top. This technique enhances retrieval quality downstream, ensuring the LLM receives the highest-confidence context for grounded, accurate answer generation.