Calibrating an agent to express uncertainty more often involves several key strategies. One primary method is to implement a confidence threshold, where the agent only provides an answer if its internal confidence score exceeds a predefined value; otherwise, it defaults to an "I don't know" response. Another crucial approach is to fine-tune the agent on a carefully curated dataset that includes numerous examples of queries for which the correct response is explicitly "I don't know," often paired with out-of-domain or ambiguous inputs. Furthermore, techniques like negative sampling during training can penalize incorrect or speculative answers, encouraging the model to abstain when unsure. Explicitly prompting the agent during fine-tuning with instructions such as "Only answer if you are absolutely certain" also reinforces this behavior. Finally, iterative evaluation and adjustment of these parameters are essential to achieve the desired balance of helpfulness and humility. More details: https://www.activealigner.pl/count.php?url=https://infoguide.com.ua