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In brief.
When a company uses AI to answer binding documents — tenders, audits, client questionnaires — an identified gap is worth infinitely more than a plausible but false answer. Yet a conversational model is optimised to satisfy the user, and therefore always to answer: abstention runs against its nature. A professional system must do the opposite: state explicitly that the information does not appear in the company’s reference documents, rather than fill the void.
Abstention changes the economics of the tool
As long as the AI answers everything, the expert has to review everything, because there is no telling where the inventions are hiding: the gain disappears into verification. As soon as the system distinguishes what it can prove from what it cannot, review becomes targeted: human attention concentrates on low-scoring answers and on gaps, while the rest is validated quickly. That triage is what creates the return on investment — not drafting speed.
The next step: dealing with the gap
Abstention on its own remains a defensive posture: saying “information missing” leaves the user empty-handed in front of a very real client requirement. A mature system chains diagnosis and treatment: requirement not covered, recommended action, remediation strategy — all before the submission date. The value lies not in noting the gap but in closing it in time.
The Optivalue.ai approach
This is the principle behind Optivalue.ai’s progressive resilience: every answer carries a confidence score from 0 to 100 and its source; below the threshold, the platform does not simply abstain, it recommends how to close the gap before submission.
Why do LLMs always answer something?
Because they are trained to produce a satisfying answer, not to qualify their own ignorance.
What is a confidence score actually for?
For organising the review: validate high scores quickly, focus experts on low ones.
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