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AI Concept

RAG (Retrieval-Augmented Generation)

Grounding an LLM's answers in retrieved documents so it responds from real, current data.

Overview

Retrieval-augmented generation is a technique that gives a large language model access to specific, up-to-date information at the moment it answers, instead of relying only on what it learned during training. When a question comes in, the system first retrieves the most relevant documents from a knowledge source, then hands them to the model as context so its answer is grounded in that material.

RAG is the standard way to build assistants that answer accurately from your own content, policies, or product data without retraining a model every time the information changes.

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
Connected in the graph

Related defined terms

Part of our defined terms knowledge graph — browse every entry in this branch.

LLM (Large Language Model)

AI Concept

An AI model trained on vast text to predict and generate language, powering tools like ChatGPT.

API

Web Dev Concept

A defined contract that lets two software systems talk to each other in a predictable way.

GEO (Generative Engine Optimization)

AI Concept

Optimising content to be cited and surfaced by AI answer engines, not just ranked in blue links.

ROAS (Return On Ad Spend)

Paid Media Metric

Revenue generated for every unit of currency spent on advertising.

Robots.txt

Technical SEO

A root-level file that tells crawlers which URLs they may or may not fetch.

Common questions

RAG (Retrieval-Augmented Generation) — questions

Straight answers on how this fits your marketing and build.

How is RAG different from just fine-tuning a model?
Fine-tuning changes the model's weights and is slow and costly to keep current. RAG leaves the model alone and feeds it fresh information at query time, so you update a knowledge base instead of retraining.
Does RAG eliminate hallucinations?
It greatly reduces them by grounding answers in retrieved sources, but it does not fully eliminate them. Retrieval quality matters, and the model can still misread or over-extend the provided context.
How does RAG reduce hallucination?
By grounding the model's answer in relevant information retrieved from a trusted source at query time, rather than relying on the model's memory. Because the answer is built from real, retrieved content — ideally with citations — the model has the correct facts in front of it, so it invents far less and its answers are traceable to a source.
What is a vector database and why does RAG need one?
A vector database stores content as embeddings — numerical representations of meaning — and lets you find the chunks most semantically similar to a query. RAG needs it to retrieve the passages relevant to a question quickly from a large corpus, so the right context can be handed to the model. Retrieval quality from this store largely determines the system's accuracy.

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