Give AI your operational definitions before asking…
Feed your operational definitions to AI systems as explicit context before generating analysis or recommendations, treating your personal glossary as the translation layer between the model's probability-weighted semantics and your specific conceptual framework.
When This Fires
- Asking AI to analyze a decision where you've defined key terms precisely (e.g., "quality," "done," "risk")
- Using AI to review writing that uses domain-specific vocabulary
- Requesting recommendations in areas where you've developed non-standard definitions through experience
- Any AI interaction where you'd correct a colleague who used your key terms loosely
Common Failure Mode
Assuming the AI "knows what you mean" because its output uses the same words you do. LLMs are fluent at producing text that sounds aligned while operating on subtly different definitions. You ask about "deep work" meaning Cal Newport's specific protocol; the AI responds about "focused concentration" — close enough to feel right, different enough to be wrong. The semantic gap is invisible because both sides use the same vocabulary.
The Protocol
Maintain a working glossary of 10-20 terms that carry specific meaning in your thinking. Before any substantive AI prompt, include the relevant definitions as explicit context: "In this conversation, 'signal' means [your definition], 'quality' means [your definition]." This takes 30 seconds and eliminates hours of misaligned output.