4 Lines You Should Include in Your Claude Skill

Large language models like Claude can produce confidently wrong analyses by overgeneralizing from limited data, such as attributing a department-wide issue to a problem with a single product. To improve reliability, specific instructions should be added to the prompt to constrain the model's behavior. Key instructions include telling the model what context it lacks, defining terms like "significant" with quantitative thresholds, and forcing it to use confidence labels for its insights. Additionally, requiring the model to explicitly state the limitations of its analysis makes the output more honest and useful for stakeholders. This iterative process of refining prompts helps calibrate the model's confidence, ensuring its language matches the strength of the evidence.