Lesson Content

A reliable GPT starts with a narrow job. Its instructions should define purpose, users, required process, output format, source priority, verification rules, prohibited behavior, and escalation conditions. Avoid vague personas without procedures.

Knowledge quality matters more than volume. Use current, authoritative, well-labeled files. Remove duplicates, drafts, and conflicting versions. State which file is the source of truth and how the GPT should handle missing information. Uploaded knowledge does not train a new foundation model; it supplies material the configured GPT can reference.

Test cases should include expected success, missing information, conflicting sources, out-of-scope requests, sensitive-data attempts, and malicious instructions in source files. Record expected behavior so updates can be regression tested. A GPT without a maintenance owner and review date will gradually become unreliable.