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To build a retrieval-augmented generation pipeline, ingest your documents, generate embeddings, store them in a vector database, and inject retrieved context into language model prompts for grounded document Q&A.

To reduce LLM hallucinations, use semantic search to retrieve relevant document context from a vector database and inject that grounding information into your language model prompts for factual accuracy.

Implement hybrid dense and sparse retrieval with metadata filtering by combining vector embeddings and keyword matching, applying metadata constraints to narrow the search scope, and reranking results for higher precision.

Yes, you can apply contextual compression and reranking to improve retrieval precision by filtering relevant document chunks and reordering search results before injecting the context into language model prompts.

Evaluate retrieval-augmented generation metrics for precision and faithfulness by measuring how accurately the injected document context answers queries and verifying the language model outputs remain grounded in cited sources.

Yes, a vector database is needed to create a knowledge-grounded chatbot because it stores generated embeddings and performs the semantic search required to retrieve relevant document context for prompt conditioning.