5.1.1 Conversation UX¶
Why This Sub-Part Matters¶
Conversation UX is the working skill inside AI Product Interface 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 Conversation UX 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: user-visible failures, clarification rate, and correction capture
- 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 user-visible failures, clarification rate, and correction capture.
Completion Standard¶
- I can explain Conversation UX 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 5.1 AI Product Interface.