If you've ever asked ChatGPT a question about your company and received a confidently wrong answer, you've experienced the fundamental limitation of standard AI: it only knows what it was trained on. Your company's documents, policies, customer data, and internal knowledge? It has no idea.
RAG (Retrieval-Augmented Generation) solves this. It gives AI agents the ability to look up real answers from your actual data before responding.
RAG in one sentence
RAG lets AI agents retrieve information from your documents, databases, and systems — so they give accurate, grounded answers based on your actual company data, not just general training knowledge.
The library analogy
Without RAG
Imagine asking a very intelligent person a question about your company. They're smart, articulate, and confident — but they've never read any of your documents. They'll give you an answer, but it might be wrong, outdated, or completely made up.
With RAG
Now imagine that same person has access to your company library. Before answering, they walk to the shelf, pull the relevant documents, read them, and then answer your question — citing the source. That's RAG.
How RAG works — four steps
Ingest
Your documents, databases, and files are processed and converted into numerical representations (embeddings).
Store
Embeddings are stored in a vector database — a specialised system optimised for similarity search.
Retrieve
When a question arrives, the system finds the most relevant chunks of your data.
Generate
The AI model uses the retrieved context to generate an accurate, grounded answer.
See it in action
Customer support agent with RAG
Customer: “What's your return policy for electronics?”
AI agent (with RAG): “According to our returns policy (updated January 2026), electronics can be returned within 14 days of purchase in original packaging. A restocking fee of 10% applies for opened items. Would you like me to start a return for you?”
Internal knowledge agent with RAG
Employee: “What's our approval process for purchases over $5,000?”
AI agent (with RAG): “Per the Finance Policy v3.2 (section 4.1), purchases over $5,000 require department head approval, followed by CFO sign-off within 48 hours. The request form is on the intranet under Finance > Procurement.”
Want to give your AI agents access to your company's knowledge? We build RAG-powered systems that turn your documents into actionable intelligence.
Book a Strategy CallRAG vs fine-tuning
These are the two main approaches to making AI “know” your data. They serve different purposes:
| RAG | Fine-tuning | |
|---|---|---|
| What it does | Gives AI access to your data at query time | Permanently changes the AI model's knowledge |
| Data freshness | Always current (reads live data) | Frozen at training time |
| Cost | Low — no model training required | High — requires GPU compute and expertise |
| Setup time | 1-2 weeks | 2-8 weeks |
| Best for | Company knowledge, documents, FAQs | Specialised language, domain terminology |
| Can combine? | Yes — RAG + fine-tuned model is the gold standard | Yes |