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01. Week 16 — Engineering Principles

🧠 Mental models

  • One-way vs two-way decisions: "a concrete door versus a revolving door"
  • ADRs: "the ship's log for why the course changed"
  • Unit tests vs evals vs monitoring: "factory checks at build time, QA on the line, and alarms in the field"
  • Technical debt: "interest charged on every shortcut"
  • API-first: "rent first, build later if the neighborhood is truly worth it"
  • Stakeholder communication: "the same engine shown through different dashboards"

⚠️ Common traps

  • Arguing about tools before stating the real decision, constraints, and reversibility.
  • Using unit tests to validate probabilistic model behavior that needs evals instead.
  • Treating prompt or model-config edits as harmless text changes with no review trail.
  • Choosing fine-tuning or self-hosting for prestige instead of evidence and operational need.
  • Letting research-heavy work escape ownership, documentation, or rollback planning.
  • Explaining architecture only in technical language and leaving product, finance, or legal unconvinced.

🔗 Prerequisites & connections

Builds on: Module 15 capstone trade-offs and the earlier modules where you learned modeling, evaluation, cost, latency, and system failure patterns.

Feeds into: Module 17 MLOps & Production, where reproducibility, versioning, release discipline, and incident response become operational requirements.

💬 Interview phrasing

  • How do you decide between build, buy, prompt-plus-RAG, and fine-tune?
  • What belongs in unit tests versus evals versus production monitoring for an ML system?
  • Tell me about a one-way door decision you would treat differently from a reversible one.
  • When is API-first the smart default instead of building in-house?
  • How would you explain the same AI architecture choice to engineering, product, and finance?

⏱️ Difficulty markers

  • 🟢 one-way vs two-way doors
  • 🟢 ADRs
  • 🟡 API-first vs build decision-making
  • 🟡 unit tests vs evals vs monitoring
  • 🟡 technical debt in AI systems
  • 🔴 stakeholder tradeoff communication

Self-check questions

  1. What makes a decision one-way versus two-way?
  2. When is API-first the smart default?
  3. What exactly belongs in unit tests, evals, and monitoring?
  4. When should an AI workflow stay manual for now?
  5. What minimum documents make a system reviewable and operable?
  6. How would you explain a model choice differently to product and finance?

Health check

By end of Week 16, you should have: - [ ] Read 02_explainer.md completely. - [ ] Written at least one ADR. - [ ] Built one risk matrix for an AI workflow. - [ ] Distinguished manual, semi-automatic, and automatic stages for one feature. - [ ] Completed the hands_on_lab and revision. - [ ] Felt ready for 04_ml_platform_operations.