what is konfidence fr?

konfidence is a small Python library that checks whether an AI language model sounds more sure of itself than the evidence it was given actually supports. Many AI mistakes are not obviously wrong answers. They are answers stated with total confidence even though the model had little or nothing to back them up, which is a much harder failure to catch by just reading the output. The library looks at three things in a piece of AI generated text: how assertive the wording is (words like definitely or it is a fact), how much supporting context the model was actually given, and how much the text hedges itself with phrases like I believe or it appears. Combining these produces a calibration score from 0 to 1, along with a plain label such as WELL_CALIBRATED, ACCEPTABLE, or SEVERELY_OVERCONFIDENT. Areas the author considers high stakes, such as legal, medical, financial, or compliance answers, are weighted more heavily, since a wrong but confident claim matters more there than in a low stakes reply. Using it is meant to be simple: you create a ConfidenceScorer, pass it the model's output plus whatever context the model actually had access to, and it returns a result object with the score, the status label, whether it should be flagged, and a list of specific reasons. It can also score many responses at once, which is useful for comparing different models, prompts, or settings against each other. Thresholds can be tightened for strict checking or loosened so only the worst cases get flagged. konfidence has no external dependencies and is meant to work with output from any language model or framework, so it can sit inside an existing pipeline as a check that runs before an answer is shown to a user or acted on. The author describes it as the open source foundation of a larger paid product aimed at governance for regulated industries, though this library itself is a standalone tool. It is released under the MIT license, so it can be used and built on freely.