Implementation
RAG: putting your company's knowledge to work
Generic AI knows the internet but not your business. Retrieval-augmented generation connects AI to your own documents - here's what that means in practice.
Ask a public AI model about your refund policy and it guesses. Ask the same model connected to your actual policy documents and it answers - with the real rule, in your words. That connection is called RAG, and it's how most useful business AI is built.
What RAG actually is
RAG - retrieval-augmented generation - is a simple loop:
- The user asks a question.
- The system retrieves the most relevant passages from your documents.
- It hands those passages to the AI model along with the question.
- The model answers using your content, not its general training.
No model retraining. No data sent off to teach someone else's model. Your knowledge stays your knowledge.
Where it pays off
- Support - answers grounded in your real policies and manuals.
- Sales - instant access to specs, pricing rules, and past proposals.
- Onboarding - new hires ask the handbook instead of interrupting colleagues.
- Operations - procedures and compliance docs, searchable in plain language.
What makes it work (or not)
- Clean source content. Messy, contradictory documents produce messy, contradictory answers.
- Good retrieval. If the system fetches the wrong passages, the model confidently answers wrong.
- Access control. People should only get answers from documents they're allowed to see.
Start small
Pick one well-documented domain - say, HR policies or product specs. Get retrieval right there, measure how often answers are correct and cited, then expand.
What we do
At Human2Human, with Meliorate Labs, we help teams understand RAG and build practical, EU-hosted assistants grounded in their own knowledge - with the access controls and human checks in place.
If you'd like to talk about your knowledge base, get in touch.