A large language model is astonishing and hopeless at the same time. It can hold a fluent conversation, but it has no idea that your early shift starts at 07:00, that travel costs are only reimbursed from 10 kilometres, or that Sam already said no twice last week. Without that knowledge every answer is a guess.
The problem: a brilliant intern with no file
Picture a brilliant new colleague who knows everything except how you do things. Ask them something and they invent a plausible answer, which may just as well be wrong. That is exactly what a bare language model does.
The temptation is to cram everything into the prompt: your whole handbook, every rule, every exception. That does not work. It gets slow, expensive, and the model drowns in noise.
The fix: the right context, at the right moment
Instead we fetch precisely the relevant knowledge per question and pass that to the model, retrieval-augmented generation, or RAG. A question comes in about travel costs? Then we look up your travel policy, and only that. Is it about a specific shift? Then the model gets the details of that shift.
The effect:
- Accurate answers that match your arrangements, not an average of the internet.
- No invented facts. The model answers on the basis of what your processes actually say.
- It scales. Whether you have ten rules or a thousand, we only retrieve what counts.
Why this makes the difference
It is the difference between an AI that says something and an AI that says the right thing. For a worker asking a question at 10pm about tomorrow, a wrong answer is worse than no answer. RAG makes sure the agent only speaks when it has the facts, and that it got them from you rather than from itself.
Generic AI is a toy. AI that knows your context is a colleague. You do not build that difference with a bigger model, you build it with better context.