Full course coming soon

Reading AI Recommendations: How to Evaluate Automated Insights

7 chapters · 30 lessons

1. 1. Anatomy of an AI Recommendation

   4 lessons

   Understand the five-stage pipeline from raw signals to suggested actions.

2. 2. Decoding Model Objectives and Incentives

   4 lessons

   Learn to identify what the system is actually optimizing for and when that diverges from your goals.

3. 3. Asking Diagnostic Questions

   5 lessons

   Develop a checklist to validate AI suggestions before acting on them.

4. 4. Evaluating Retrieval and Generation Quality

   4 lessons

   Assess AI-generated answers, summaries, and search results for relevance and hallucination risk.

5. 5. Designing Human-in-the-Loop Workflows

   5 lessons

   Structure processes that use AI for speed while preserving judgment and accountability.

6. 6. Recognizing Data Artifacts and Bias

   4 lessons

   Identify when recommendations reflect data quality issues rather than real patterns.

7. 7. Building Your AI Evaluation Toolkit

   4 lessons

   Assemble templates, checklists, and habits for ongoing critical use of AI tools.