5.6.4 Feedback Loops and Data Flywheels¶
Why This Sub-Part Matters¶
Feedback Loops and Data Flywheels is the working skill inside Application Evaluation and Feedback that helps you build the stage artifact, An evaluated RAG or AI workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis, while collecting enough evidence to trust the result. A sub-part is now a folder so longer topics can grow without forcing everything into one huge page.
Study Pages¶
| Page | Purpose |
|---|---|
| Deep Dive | Full explanation, mechanisms, examples, and failure modes. |
| Examples and Practice | Worked exercises, project drills, and self-check prompts. |
Core Ideas¶
- Define Feedback Loops and Data Flywheels in plain language before naming tools or frameworks.
- Connect it to the stage artifact: An evaluated RAG or AI workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis.
- Measure it with: pass rate, judge agreement, human review notes, and feedback categories
- Name at least one failure mode, because real AI engineering is mostly controlled failure reduction.
- Keep the first implementation small enough to inspect by hand before scaling it.
How to Study It¶
- Read this overview and write the concept in your own words.
- Read the deep dive and identify the input, transformation, output, and failure mode.
- Complete the examples and practice page.
- Add one measurement using: Track pass rate, judge agreement, human review notes, and feedback categories.
Completion Standard¶
- I can explain Feedback Loops and Data Flywheels without naming a tool first.
- I can connect it to the stage artifact.
- I can show a small artifact, measurement, or test.
- I know how it fails and what I would inspect first.
Return to 5.6 Application Evaluation and Feedback.