A real scenario — an engineer asking why the office network is slow — walked through scene by scene.
An engineer asks the company's internal AI assistant a question that comes up in almost every interview about "what could go wrong in production": the network is misbehaving.
Ask a plain LLM with no access to your company's actual documents, and it can only draw on generic knowledge from training — it has never seen your office's floor plan, your router model, or last month's ticket history.
Not wrong, exactly — just not useful. It's the same answer it would give any company, in any building, on any day.
Before answering, the system searches the company's own "Network Troubleshooting Handbook" — a real internal document — for passages related to the question, and pulls back the most relevant one.
The retrieved passage isn't shown to the user directly — it's quietly added to the prompt alongside the original question, so the model answers with real context instead of guessing.
Same question, same model — but now it can point at the actual cause and where it came from, not a guess.
Retrieval-Augmented Generation: search your own documents first, then let the model answer using what it found — instead of hoping it already knows. This site runs a real, working implementation of it.
See the real local RAG app →