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Evaluating AI Outputs: Statistical Patterns vs. Verified Reasoning

7 chapters · 29 lessons

1. 1. Pattern Reproduction vs. Reasoning: What Neural Networks Actually Do

   4 lessons

   Understand the statistical foundations of neural networks and why fluent outputs do not guarantee correctness.

2. 2. Designing Ground Truth Evaluation Protocols

   4 lessons

   Build testing frameworks that measure correctness rather than plausibility or tone.

3. 3. Risk Mapping: Where Human Verification Is Non-Negotiable

   4 lessons

   Learn to classify tasks by consequence and design appropriate oversight mechanisms.

4. 4. Quality Assurance Checkpoints for Production Systems

   4 lessons

   Implement staged review processes that catch errors before they reach end users.

5. 5. Shaping Statistical Context: Prompt Engineering and RAG

   4 lessons

   Understand how interventions like prompting and retrieval steer probability distributions rather than program logic.

6. 6. Communicating AI Capabilities to Stakeholders

   4 lessons

   Translate architectural principles into clear expectations for executives, clients, and compliance teams.

7. 7. Decision Trees for Trust, Verify, or Reject

   5 lessons

   Create operational guidelines for when to accept, review, or discard AI outputs in your specific context.