NeuralSeek · AI Grounded
Answer ConfidenceForce KB: refuse to answer from anything but your knowledge base
Force KB locks the assistant to your knowledge base, guaranteeing every answer reflects approved sources rather than the model's general training.
Force KB is one of NeuralSeek's Answer Confidence guardrails — part of the platform's 118 individually configurable, fully auditable controls. In regulated, high-volume AI, the difference between a system you can trust and one you merely hope works comes down to specific, tunable controls exactly like this one. Here is what Force KB does, why it matters to the business, and how to set it for your own environment.
What it actually does
This forces the system to answer only from the knowledge base, refusing to draw on the model's general training. The model literally cannot answer outside your sources.
Why business teams care
It's the strictest grounding perimeter there is — the guarantee that answers reflect your content, not the model's opinions. For regulated and high-trust use cases, that guarantee is non-negotiable.
How to tune it in practice
Enable it wherever answers must come strictly from approved sources. Pair it with strong retrieval so the locked-down system still has good material to draw from.
Common failure modes it prevents
The most dangerous answer is a confident one the system can't actually stand behind, delivered with the full authority of the assistant. Force KB closes that gap directly. By making the behavior an explicit, enforced control rather than something left to chance, it converts a latent risk into a managed, observable event — one that surfaces in the audit trail instead of in a customer complaint or a compliance finding.
Where it fits in the stack
It sits at the answer-quality gate, after grounding and before delivery, deciding whether a response is good enough to ship. Because it lives in NeuralSeek's governance layer rather than inside any single model, the control holds identically whether a request routes to OpenAI, Anthropic, Gemini, Llama, Mistral, IBM watsonx, or an in-house model.
Confidence as an explicit policy
By turning 'how sure must we be?' into a number you set on purpose, this control lets the business decide its own tolerance for uncertainty — consistently, and the same way every time.
The safest model is one that can only tell you what your own sources say.
The takeaway
Force KB locks the assistant to your knowledge base, guaranteeing every answer reflects approved sources rather than the model's general training.