Retrieval-Augmented Generation RAG AI

Retrieval-Augmented Generation RAG AI is a system pattern that couples a large language model (LLM) with an external search component to ground every answer in fresh, verifiable data. At run-time the retrieval stage converts the user query into embeddings, searches a vector or hybrid index, and returns the top-k passages plus metadata. In the generation stage those passages are injected into a templated prompt, enabling the LLM to weave citations and context into its response while minimizing hallucinations. Engineers fine-tune chunk size, similarity thresholds, rerankers, and prompt budgets to balance latency, recall, and token cost. Because knowledge is fetched on demand rather than baked into model weights, RAG AI updates instantly when documents change, slashing retraining cycles. The pattern powers chatbots, analyst copilots, compliance tools, and search interfaces that demand auditability and rapid content refresh. Guardrails such as PII scrubbing, confidence scoring, and fallback workflows elevate enterprise trust while automated evaluations—precision@k, faithfulness—streamline continuous improvement.