Examples and Practice: Feedback Loops and Data Flywheels

Worked Practice

1. Write one paragraph explaining Feedback Loops and Data Flywheels to a beginner.
2. Draw the smallest diagram that shows input, transformation, output, and failure mode.
3. Build or outline a tiny artifact connected to: Create a 50-question eval suite and feedback flow.
4. Measure it with: Track pass rate, judge agreement, human review notes, and feedback categories.
5. Add one failure case to your learning log.

Mini Project Drill

Create a file named notes/feedback-loops-and-data-flywheels.md in your project workspace. Include:

• the problem Feedback Loops and Data Flywheels solves
• the simplest implementation or design
• the measurement you used
• one example input
• one expected output
• one failure case
• one decision you would make from the result

Check Your Understanding

Question	What a strong answer includes
Why does Feedback Loops and Data Flywheels matter?	It connects to an evaluated rag or ai workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis. and names a practical risk.
How would you test it?	It uses a small repeatable case and a measurable expected result.
What breaks first?	It names a specific failure mode, not only "the model is bad".
When should you move on?	When the artifact works on a realistic case and one edge case.

Stretch Exercise

Revisit the same drill after finishing the next part. Update the note with what changed. This is how isolated concepts become connected system judgment.

Return to 5.6.4 Feedback Loops and Data Flywheels.