AI Glossary

Retrieval-Augmented Generation (RAG)

What it means, why it matters to your business, and where it shows up in a real build decision.

Also known as RAG

In plain terms

Retrieval-augmented generation lets an AI model answer using your own documents by fetching the relevant passages at the moment of the question. RAG is how you get an AI assistant that knows your policies, contracts, or product docs without retraining a model. It is the default approach for most business knowledge tasks.

A simple analogy

Without RAG, an LLM is a well-read expert answering from memory. With RAG, that expert pulls the relevant pages from a file cabinet before answering, so the answer is grounded in your actual documents.

What it looks like in practice

An insurance company builds a claims assistant that searches their policy library in real time. Adjusters get answers grounded in the exact policy version, reducing escalations and errors.

When to use it

Whenever the AI needs to answer from your specific content: policies, product docs, contracts, knowledge bases. It is the default architecture for enterprise knowledge work.

When to avoid it

When the task does not require specific factual retrieval, such as creative drafting or general summarization. Adding retrieval to those tasks adds latency and cost without meaningful benefit.

Put this to work on a real problem.

Tell us what's slowing you down and we'll show you where RAG development fits.

RAG development

Work with us

Tell us what's broken.

Tell us what's not working in your business. We'll find the real problem and tell you exactly what it would take to fix it.

Scope and cost agreed before work starts. No surprises. No obligation.
Working prototype within 3 weeks of kickoff.
Pay by milestone. You see progress before each invoice.
60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
All conversations are NDA-protected.