Module 10: Evaluating and Improving Outputs - Wrap-up
Reviewing the critical strategies for accuracy, objectivity, and quality control.
Module 10 Wrap-up: The Quality Architect
You have now moved beyond just "getting an answer." You have the tools to ensure that answer is Accurate, Unbiased, and Exceptional.
What We Covered
- Lesson 1: Verification strategies to cross-check AI facts with reality.
- Lesson 2: Minimizing hallucinations and handling bias with anchor patterns.
- Lesson 3: The "Critique-and-Revise" loop for advanced refinement.
- Lesson 4: Using Rubrics to set high expectations for the AI.
- Lesson 5: Implementing Human-in-the-loop (HITL) for high-stakes work.
Key Vocabulary
| Term | Definition |
|---|---|
| Grounding | Using specific data to keep the AI anchored in truth. |
| Rubric | A set of criteria used for scoring an output. |
| HITL | Human-In-The-Loop. |
| Post-Mortem | Asking the AI to analyze its own failure. |
Quick Quiz
- Why is a specific "Rubric" better than a generic instruction?
- What are "Anchor Facts" and why do they prevent hallucinations?
- What is the difference between a "Low-Stakes" and "High-Stakes" output?
What's Next?
You have all the technical and strategic skills. Now, let's look at the "Pro Tips." In Module 11: Tips, Tricks, and Best Practices, we'll cover the time-saving keyboard shortcuts, the hidden settings, and the "AI Playbook" that will keep you ahead of the curve as the technology evolves.