RAG explained for a Product Owner: when and why to use it

RAG comes up in almost every conversation about AI agents. “We’ll do RAG” has become a reflex answer to many problems. Here’s what it really means, and when it’s actually the right solution.

The problem RAG solves

An LLM has two fundamental limitations:

Knowledge cutoff. Models are trained up to a certain date. They don’t know your internal data, recent documents, or knowledge base.

Context window. Even with 200k tokens, you can’t put everything in every request. On a 10,000-page document base, it’s impossible.

RAG solves both problems by combining search in your data with LLM generation.

How it works, simply

1. Your documents are split into chunks and transformed into vectors (embeddings)
2. These vectors are stored in a vector database
3. When a user asks a question, it’s also transformed into a vector
4. We search for document chunks closest to the question
5. These chunks are injected into the LLM context with the question
6. The LLM answers based on these excerpts

Use cases where RAG shines

FAQ and customer support on proprietary documentation. This is the most mature use case. An agent that answers questions based on your manuals, internal procedures, knowledge base.

Search in large archives. Contracts, emails, reports — when volume exceeds what can be put in context.

Business agent with product reference data. A sales agent that can answer precise questions about your catalog, pricing, terms.

When RAG is not the solution

When the context window is sufficient. If your documents are less than 50 pages, put them directly in context. Simpler, more reliable.

When the problem is LLM quality, not data. I’ve seen teams implement RAG to “improve responses” when the real problem was a poorly written system prompt.

When you haven’t validated the use case yet. RAG adds complexity (ingestion pipeline, vector database, embedding management). Don’t do it on a POC — start with direct context.

What you need to know as a PO

Document chunking quality is critical. Poor chunking (too small, too large, poorly structured) degrades results even with the best LLM.

Evaluation metrics are different. Beyond evaluating LLM responses, you must evaluate retrieval quality: are the right excerpts being found for each question?

RAG doesn’t eliminate hallucinations. It reduces them by giving the model sources, but an LLM can still ignore sources and fabricate. You need to explicitly test this behavior.

Costs are more complex. LLM API + vector database hosting + ingestion pipeline + embedding costs. Price everything before committing.