What an AI model can and cannot know
Understand what an AI model can and cannot know through practical distinctions, a worked example, and checks you can apply to a real task.
Understand the system before using it
Durable concepts about model knowledge, context, grounding, data, cost, and risk.
Context versus model knowledge
Understand context versus model knowledge through practical distinctions, a worked example, and checks you can apply to a real task.
Hallucinations and unsupported claims
Understand hallucinations and unsupported claims through practical distinctions, a worked example, and checks you can apply to a real task.
Current facts and source checking
Understand current facts and source checking through practical distinctions, a worked example, and checks you can apply to a real task.
Grounding and retrieval
Understand grounding and retrieval through practical distinctions, a worked example, and checks you can apply to a real task.
Data sensitivity and approved environments
Understand data sensitivity and approved environments through practical distinctions, a worked example, and checks you can apply to a real task.
Inherent task risk versus safeguards
Understand inherent task risk versus safeguards through practical distinctions, a worked example, and checks you can apply to a real task.
Cost per completed task
Understand cost per completed task through practical distinctions, a worked example, and checks you can apply to a real task.