Vector Databases and RAG: Building Context-Aware Generative Agents

Large language models suffer from hallucinations and static knowledge cutoffs. Retrieval-Augmented Generation (RAG) mitigates this by querying external vector databases (e.g., Pinecone, Milvus) for semantically relevant document chunks using cosine similarity. We discuss indexing methods, prompt synthesis, and retrieval evaluation metrics.