Collecting User Feedback in LLM Apps

Production feedback can reveal problems that a fixed evaluation set misses. Explicit signals include ratings, thumbs up or down, written comments, and user edits. Implicit signals include regenerating an answer, copying text, abandoning a session, or repeatedly asking the same question. None of these actions has a single guaranteed meaning: a copy may indicate usefulness or may be needed to save text, and an abandoned session may reflect interruption rather than failure. A useful feedback workflow connects a signal to enough context to investigate the issue, such as a versioned trace identifier, task type, model version, and outcome. It should avoid collecting more content than necessary. If prompts, outputs, or files are shared with a provider or used for evaluation or training, the organization should understand the provider’s current data controls and obtain required permissions. OpenAI’s API data-sharing guidance, for example, describes feedback sharing as opt-in and notes that a thumbs-down submission may include the conversation up to that point and uploaded files. Feedback is biased toward people who choose to respond and toward experiences that provoke a response. Ratings can vary by user expectations, accessibility, language, or context. Do not treat an unrepresentative thumbs-up sample as proof of quality or an implicit action as consent to share content. Triage feedback with clear categories, privacy and retention rules, and human review. Remove or protect sensitive data before creating evaluation examples. Track whether a fix improves task outcomes across user groups. Feedback should complement structured tests, expert review, and safety monitoring rather than replace them.