RAG (Retrieval-Augmented Generation)

How it works

Your documents are split into chunks and converted into embeddings stored in a vector database. At query time the system finds the chunks most relevant to the question, injects them into the prompt, and the model composes an answer from that retrieved context, ideally citing it.

• Retrieval finds relevant passages from your knowledge base
• The passages are added to the prompt as grounding context
• The model answers from that context instead of from memory alone

Why it matters

RAG dramatically reduces hallucination and lets an assistant stay current: you update the knowledge base, not the model. It also keeps proprietary data out of training and lets answers cite their sources, which builds trust.

How REO Rank applies it

We build RAG knowledge assistants over a client's documentation, support content, and product data, so staff and customers get accurate, cited answers grounded in the client's own material rather than a model's guesswork.

Why it matters

Retrieval-Augmented Generation (RAG) is the technique of giving a language model relevant information retrieved from a trusted source at query time, so it answers from that information rather than only its frozen training knowledge. It matters because it solves the two biggest problems with using LLMs in real products: hallucination (the model inventing answers) and staleness (the model not knowing your data or recent facts). RAG is the standard architecture behind AI assistants that answer accurately over a company's documents, a product's knowledge base, or any specific, current information the base model does not contain.

• Gives an LLM retrieved, trusted information to answer from at query time
• Solves the two big LLM problems: hallucination and stale/unknown data
• The standard architecture for accurate AI assistants over your own content

How it works

A RAG system has two phases. First, ingestion: your documents are split into chunks, converted into vector embeddings that capture their meaning, and stored in a vector database. Then, at query time: the user's question is embedded, the most semantically relevant chunks are retrieved from the database, and those chunks are inserted into the prompt as context alongside the question, so the model generates an answer grounded in them — ideally with citations back to the source. Because the answer is built from retrieved, real content rather than the model's memory, it is far more accurate, current and traceable.

• Ingest: chunk documents, embed them as vectors, store in a vector database
• Query: embed the question, retrieve the most relevant chunks
• Insert retrieved chunks into the prompt as grounding context
• The model answers from real content, ideally with source citations

In practice

Building a good RAG system is more than wiring an LLM to a vector store — retrieval quality is everything, because the model can only be as accurate as the chunks it is given. That means careful chunking, good embeddings, and often reranking or hybrid (keyword + semantic) retrieval to surface genuinely relevant context. Production RAG also needs access control (users should only retrieve from content they are allowed to see), evaluation of answer quality against real questions, and guardrails. Done well, RAG turns a generic model into a trustworthy expert over your specific knowledge; done carelessly (poor retrieval, no access control) it produces confident answers from the wrong chunks.

• Retrieval quality decides answer quality — invest in chunking and embeddings
• Reranking and hybrid retrieval improve which chunks are surfaced
• Add permission-aware retrieval so users only see allowed content
• Evaluate answer quality and add guardrails for production use