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Learning Path

AI Foundations

Build a durable mental map of how modern AI models behave, fail and are governed.

Learning goal
Understand the model/runtime boundary well enough to reason about any later AI system.
What you will build
A reusable mental model for model behavior, limits, lifecycle and responsibility.

Local learning evidence

0 / 34 available Guides read0 / 1 Practices completed

Counts only published Guides and Practice completion signals; page views are never presented as mastery.

Continue learning: Probabilistic model behavior

Course structure

01
02

Instructions, reasoning and limits

14 topics
  1. Instruction authorityInstruction authority and provenanceGuide
    7 min
  2. Instruction conflictAmbiguity, specificity and instruction conflictGuide
    7 min
  3. Examples shape behavior; they do not enforce policyConcept · Guide expanding
  4. Prompt, context and runtime boundaryPrompt vs Context vs Runtime responsibility boundaryGuide
    7 min
  5. Prompt specificitySpecificity without overconstraintGuide
    7 min
  6. Prompt decompositionDecompose instructions around decisionsGuide
    6 min
  7. Reasoning budgetReasoning has a cost and budgetGuide
    7 min
  8. Planning as search over alternativesConcept · Guide expanding
  9. Verification over introspectionVerification beats introspectionGuide
    6 min
  10. Reasoning opacity and observable evidenceConcept · Guide expanding
  11. Hallucination as unsupported generationGuide
    7 min
  12. Uncertainty calibrationUncertainty and calibrationGuide
    6 min
  13. Distribution shiftGuide
    7 min
  14. Capability is not a guaranteeGuide
    7 min
03