Everyday use of ChatGPT spans study and work, but surveys and usage logs capture different parts of that behaviour. This thesis compares what people say in a short survey with what their anonymised ChatGPT export shows, without linking individuals. The design is simple and auditable: donors share platform-native logs, prompts are mapped to clear task families using compact example prototypes and all comparisons are made at the group level. To keep both sources directly comparable, the same frames are used on each side: intensity (how often and how long), timing (broad dayparts on one time base), input form (prompt-length bands) and task portfolio (main task families with concise subtasks). Analyses focus on full distributions and effect sizes rather than single averages. The core message is practical: self-reports give a workable signal for “how much,” while donation-based logs add detail on “how” people interact and “what” they use the tool for. Short, one-line, iterative or more technical exchanges are easy to miss in surveys, so using both sources together gives a more realistic picture for policy, training and procurement. The thesis closes with guidance on measuring and monitoring everyday use in organisations and education, with privacy and reproducibility built in.