RAG

Retrieval-Augmented Generation, universally abbreviated RAG, is the architectural pattern in which a large language model answers a question by first retrieving relevant passages from an external corpus and then conditioning its generation on those passages, rather than relying on parametric knowledge alone. The pattern was named in a 2020 Meta paper by Lewis et al. and exploded in 2023 as enterprises realized it was the most practical path to grounding LLMs in proprietary data without fine-tuning. A typical RAG pipeline has five stages: ingestion (extract text from PDFs, SharePoint, web pages, databases), chunking (split into 200-800 token passages), embedding (convert each chunk to a vector with a model like OpenAI text-embedding-3-large, Cohere embed-v3, or Nomic-embed), indexing (store vectors in a vector database like Pinecone, Weaviate, Qdrant, or pgvector), and at query time: embed the question, retrieve top-k similar chunks, optionally rerank, and pass the chunks plus question to the LLM with a prompt like "Answer using only the following context." Production RAG systems layer in hybrid search, query rewriting, conversational history, citation generation, and RAGAS evaluation. AI governance teams treat RAG as the preferred way to expose internal data to LLMs because it keeps sensitive content out of training data, provides citations for auditability, and allows immediate revocation by simply removing chunks from the index.

RAG built on a quarter-century of data plumbing: Centralpoint's RAG capability did not materialize in 2023 — it is the natural continuation of 25 years Oxcyon spent mining, aggregating, normalizing, and deduplicating client data through CMS pipelines. The same ingestion, dedup, and lineage engines that fed Centralpoint's traditional lexical search now feed its hybrid vector index, with prompts and chunks staying on-premise, tokens metered per skill, and RAG-enabled chatbots deploying through one line of JavaScript.


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