06. Module 16 Revision — Engineering Principles¶
File loop¶
- Re-skim 02_explainer.md headings only.
- Re-read the tables in 03_study_material.md.
- Answer 04_daily_recall.md cold.
- Review your deliverables from 05_hands_on_lab.md.
One-page summary check¶
Can you explain these in plain language? - Why principles matter more at Lead level. - How build vs buy vs fine-tune should usually be sequenced. - Why reversibility changes process overhead. - Why AI quality needs unit tests, evals, and monitoring. - When manual review is not cowardice but good engineering. - Why documentation is the ship's log, not a side task.
Failure-fix recap¶
| If you see this | Reach for this principle |
|---|---|
| Repeated architecture debate | Write ADRs and decision criteria |
| Demo velocity, no memory | Improve documentation habits |
| Model blamed for every issue | Separate test, eval, and monitor layers |
| Unsafe automation pressure | Use staged automation and risk review |
| Research work missing dates | Time-box questions and exit criteria |
| Stakeholder magical thinking | Translate capability bands and tradeoffs |
| Incident chaos | Add traces, versions, and rollback discipline |
| Slow onboarding | Standardize README, ADR, eval spec, runbook |
Readiness gate for Module 17¶
You are ready for 04_ml_platform_operations if you can do all four below without hand-waving.
1. Decision framework basics¶
- [ ] I can defend a major AI architecture choice with criteria and tradeoffs.
- [ ] I can explain when I would revisit that choice.
2. Automate vs manual¶
- [ ] I can say which stage of automation a feature deserves today.
- [ ] I can name the evidence needed to automate further.
3. Risk assessment¶
- [ ] I can think in blast radius, failure modes, and rollback paths.
- [ ] I can explain the weather check before launch.
4. Documentation habits¶
- [ ] I can write a short ADR without drama.
- [ ] I can name the minimum docs that make an AI system operable.
Interview prompts¶
Practice these aloud: 1. “Tell me about a technical decision you made under ambiguity.” 2. “When would you avoid fine-tuning?” 3. “How do you review AI changes that are partly prompt and partly code?” 4. “How do you decide what stays manual?” 5. “How do you keep a fast team from becoming a chaotic team?”
Final reflection¶
Write short answers: - Which principle from this module do you already use naturally? - Which one do you still resist? - Where in your current work is the ship's log weakest? - What is one decision you now want to document properly?
Bridge forward¶
Next module — 04_ml_platform_operations — turns this decision framework into infrastructure: CI/CD for ML, model registries, monitoring, and the platform that makes good engineering automatic.