Confidence Score
Definition
A rating an AI system attaches to each of its own outputs indicating how well-grounded the answer is in source material. In RFP analysis, every requirement match is scored (e.g. full match, partial match, gap) with a confidence level; low-confidence answers are flagged for human review by default, so SE attention goes where the risk actually is.
It exists to allocate attention
Nobody reviews three hundred requirement matches with equal care. A score that reliably separates well-grounded answers from weakly-grounded ones lets an SE spend their review time on the twenty that could embarrass them. Without it, review is uniform and shallow, which is the same as no review on the answers that mattered.
A number without a reason gets ignored
A bare percentage teaches nobody anything and is quietly disregarded after the first time it is wrong. A score that shows what it was based on — which document, which passage, how directly it addressed the question — can be checked, and a score that can be checked eventually gets trusted.
Calibration is the whole claim
If answers marked high-confidence turn out wrong as often as the rest, the score is decoration. It has to be validated against outcomes, not just displayed.
Related terms
Further reading
- AI RFP Analysis: From a 150-Page RFP to Scored Requirements in Hours
- When the AI Is Wrong: Hallucinations in PreSales
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