Retrieval-Augmented Generation RAG applications

Retrieval-Augmented Generation RAG applications are software solutions that fuse a search engine with a large language model (LLM) to deliver grounded, up-to-date answers. Typical use cases include AI chatbots that reference product manuals, internal search portals that surface policy excerpts, research copilots that cite scientific papers, and compliance auditors that scan contracts for risky clauses. Each application follows the same two-step loop: retrieve top-k passages from a vector or hybrid index and have the LLM draft a response conditioned on those passages. Engineers tune chunk sizes, similarity thresholds, and prompt templates to optimize recall, latency, and token cost. Because knowledge lives outside model weights, RAG apps update instantly when documents change, eliminating retraining cycles and reducing hallucinations. Guardrails—PII scrubbing, confidence scoring, fallback workflows—make these applications enterprise-ready, while evaluation metrics such as precision and faithfulness scores enable continuous improvement.