The problem RAG solves
A general-purpose LLM doesn't know your company's documents, your product catalog, or last week's support tickets — it only knows what it was trained on, which has a cutoff date and doesn't include anything private to your business. RAG is how you give a model access to that information without retraining it.
How it actually works
At query time, the system searches your own data — documents, a knowledge base, a database — for the pieces most relevant to the question being asked, then hands those pieces to the LLM alongside the question. The model answers using that retrieved context instead of relying purely on what it memorized during training. That's the whole idea: retrieve relevant information, then generate an answer grounded in it.
Why almost every business "AI assistant" is actually RAG
When a company says "we built an AI assistant that answers questions about our product docs" or "our AI searches our internal knowledge base," that's RAG in nearly every case — not a custom-trained model. It's the most common, most cost-effective way to make an LLM useful for business-specific information, which is why it dominates real-world AI development work.
What RAG is good at
- Answering questions grounded in specific documents, policies, or product information
- Keeping answers current — update the underlying documents and the assistant's knowledge updates immediately, no retraining needed
- Citing sources, since the system knows which document a piece of context came from
What RAG isn't good at
RAG doesn't teach a model a new skill or a new way of reasoning — it gives it new information to reason with. If what you actually need is a model that behaves differently (a specific tone, a specialized task format), that's a fine-tuning problem, not a retrieval problem. See RAG vs. Fine-Tuning — Which Approach Fits Your Product? for that distinction in full.
What it costs to build
A well-scoped RAG implementation is one of the more predictable AI builds — see AI Chatbot Development Cost for realistic ranges.
Where we've built this
RAG architecture is core to how we approach the AI Integration service — document intelligence pipelines, internal knowledge search, and custom assistants all typically start here. Talk to us if you're evaluating whether your use case fits.