RAG: How AI Systems Look Things Up

What it is

RAG is a technique that gives a language model access to a knowledge base at query time. Instead of relying purely on what the model learned during training, it retrieves relevant documents and uses them to inform the response. This means the model can answer questions about information that was not in its training data, including your private data. The result is responses that are grounded, specific, and traceable back to a source.

How it works

When a user asks a question, the system converts it into a vector and searches a database of pre-indexed documents for the closest matches. The top results are pulled and sent to the LLM alongside the original question. The model uses them as context to generate a grounded, specific answer. The quality of the output depends on both how well the documents are indexed and how well the retrieval step finds the right ones.

Real example

A customer service bot for a B2B SaaS product. Instead of hallucinating policy details, it retrieves the actual support documentation and answers from that. The same LLM, much more reliable output. Users get answers that cite real documentation rather than confident-sounding guesses.

When you need it

• Your AI needs to answer questions about private or proprietary data.
• You need answers to stay current without retraining the model.
• You need to be able to trace where answers came from.