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Closed-Loop AI Systems: Validation, Feedback, and Workflow Design

7 chapters · 33 lessons

1. 1. From Predictions to Outcomes: Closed-Loop System Fundamentals

   4 lessons

   Understand why feedback loops matter more than model accuracy and map the components of production AI systems.

2. 2. Hypothesis Generation and Context Retrieval Architecture

   5 lessons

   Design the front end of your loop: grounding model outputs in trusted knowledge and domain constraints.

3. 3. Execution Pipelines: Connecting Models to Real-World Actions

   5 lessons

   Build the infrastructure that takes model outputs and executes them safely in software, hardware, or human workflows.

4. 4. Measurement and Validation in Regulated and Physical Domains

   5 lessons

   Design measurement systems that capture outcome quality, not just output fluency, across diverse validation contexts.

5. 5. Feedback Loops and Continuous Model Improvement

   5 lessons

   Close the loop by using execution results to improve prompts, datasets, evaluation criteria, and model training.

6. 6. Human-in-the-Loop and Governance Patterns

   4 lessons

   Integrate human review, escalation, and oversight into automated workflows without creating bottlenecks.

7. 7. Deployment Architectures: Cloud, Edge, and Hybrid Systems

   5 lessons

   Choose and implement deployment patterns that support closed-loop learning across infrastructure constraints.