LLMs + RAG: Turning Generative Models into Trustworthy Knowledge Workers

Large language models are powerful communicators but poor historians — they generate fluent answers without guaranteed grounding. Retrieval‑Augmented Generation (RAG) is the enterprise-ready pattern that remedies this: it pairs a retrieval layer that finds authoritative content with an LLM that synthesizes a response, producing answers you can trust and audit.

How RAG works — concise flow

• Index authoritative knowledge (manuals, SOPs, product specs, policies).
• Convert content to searchable artifacts (text chunks, vectors, or indexed documents).
• At query time, retrieve the most relevant passages and pass them to the LLM as context.
• The LLM generates a response conditioned on those passages and returns the answer with citations or source snippets.

RAG architectures — choose based on needs

• Vector-based RAG: semantic search via embeddings — best for unstructured content and paraphrased queries.
• Retriever‑Reader (search + synthesize): uses an external search engine for candidate retrieval and an LLM to synthesize — balances speed and interpretability.
• Hybrid (BM25 ...