Putting generative models to work on your content

A chat window is one way to expose it, not the mechanism itself. The same retrieval-and-grounding setup drafts support replies, summarizes documents, or powers an internal search that answers in sentences rather than links. What matters is that the answer comes from your own content, not the model's general training.

A language model predicts the next plausible word, not a verified fact. Left alone, it fills gaps with the most statistically likely phrasing, in the same confident tone whether the claim is true or invented — which is why a wrong answer reads exactly like a right one. Grounding fixes this: the model only draws from the passages retrieval hands it.

Usually not. Retrieval-augmented generation grounds an existing model in your content without training a new one — most of the engineering work is in retrieval, guardrails and evaluation. Fine-tuning is a separate, narrower option, worth it only when grounding alone can't enforce a required tone or format.

It should say so. Part of building this system is defining the refusal — the exact conditions under which the model declines to answer, cites uncertainty, or hands off to a person — before launch, not afterward. A system with no defined refusal will guess, and a confident guess is the failure mode that costs the most trust.

When source content changes faster than it can be re-indexed, or the documents themselves are unreliable or contradictory, grounding a model in bad information just produces confident, well-cited nonsense. It is also the wrong tool when the real questions have a small, fixed set of correct answers — a search index answers those without the cost of a language model.