Skip to content

module-content-qa-checklist.md

Latest commit

 

History

History
144 lines (119 loc) · 4.97 KB

module-content-qa-checklist.md

File metadata and controls

144 lines (119 loc) · 4.97 KB

Module Content QA Checklist

Use this checklist after generating each module’s lecture-notes.html, lab.html, lab-solution.html, and slides.html.

1. Pedagogy And Depth

  • Does the module start from the large picture before details?
  • Does it explain why the topic matters before mechanics?
  • Does it build clear mental models?
  • Does it include deep insights rather than only definitions?
  • Does it explain how the topic connects to production AI engineering?
  • Does it clearly show how the concepts fit together?
  • Does it explain how the module connects backward to previous modules and forward to later modules?
  • Are there misconceptions or failure modes explicitly addressed?

2. Completeness Of Deliverables

  • Are all four files present?
  • Is lecture-notes.html present?
  • Is lab.html present?
  • Is lab-solution.html present?
  • Is slides.html present?

3. Lecture Notes Quality

  • Does the lecture notes page include:
    • module title
    • module purpose
    • why the module matters in the course
    • prerequisites from earlier modules
    • learning outcomes
    • a large-picture map or orientation section
    • mental models
    • examples
    • production implications
    • misconceptions or failure modes
    • sources
  • Are the explanations detailed and informative?
  • Are bridge/connection callouts present between major sections?
  • Are deep-insight callouts present where useful?
  • Are common misconceptions called out where useful?
  • Are the sections sequenced logically?

4. Examples And Practicality

  • Are there many concrete examples?
  • Are examples realistic rather than purely toy examples?
  • Are there relevant code examples?
  • Do examples show real AI engineering system thinking?
  • Are there contrast examples where useful:
    • bad vs better framing
    • prototype vs production
    • brittle vs robust
    • unsafe vs safer
  • Is the module hands-on in spirit, not just descriptive?

5. Visuals And Illustrations

  • Are there enough diagrams and illustrations?
  • Are diagrams pedagogically useful rather than decorative?
  • Is there at least one strong large-picture diagram?
  • Are there system diagrams where architecture matters?
  • Are there flow/lifecycle diagrams where workflows matter?
  • Are there failure-mode or tradeoff diagrams where appropriate?
  • If third-party images are used, are they clearly labeled with sources?
  • If Gemini-generated visuals are used, was .env.all used for the Gemini API key?

6. Research Quality

  • Were modern, authoritative sources used?
  • Were primary sources preferred for technical content?
  • Are source links included in the lecture notes?
  • Are claims consistent with current AI engineering practice?
  • Is the module modern and up to date?

7. Cross-References

  • Does the lecture notes page cross-reference earlier modules when older concepts are reused?
  • Do those references explicitly name the prior concept/module?
  • Does the lecture notes page link to:
    • this module’s lab
    • this module’s lab solution where useful
    • this module’s slides where useful
  • Does the lab link to exact supporting lecture sections?
  • Does the lab solution link to exact supporting lecture sections?
  • Do the slides link back to notes and lab?
  • Do the slides reference earlier modules when relying on prior concepts?
  • Do anchors and links actually work?

8. Lab Quality

  • Does the lab include:
    • objectives
    • preparation
    • exercises/challenges
    • deliverables
    • rubric
    • wrap-up
  • Are the exercises realistic and production-oriented?
  • Can a student run the lab without missing context?
  • Is the lab clearly tied to the lecture material?

9. Lab Solution Quality

  • Does each exercise solution include:
    • the complete original assignment/challenge description
    • the expected deliverable
    • supporting lecture links
    • the worked solution
    • explanation of why the solution is strong
  • Is there a final “full lab challenge at a glance” summary?
  • Is the solution pedagogically useful, not just terse?

10. Slides Quality

  • Are the slides interactive HTML, separate from the notes?
  • Do the slides have:
    • previous/next controls
    • keyboard navigation
    • progress indicator
    • overview/jump mode
  • Do the slides present a coherent live-teaching sequence?
  • Do they preserve intuition-first pedagogy?
  • Do they distill the lecture rather than merely copying it?
  • Are the slides visually clear and presentation-friendly?
  • Are the links back to lecture notes/lab/lab solution present?

11. Technical Validation

  • Is the HTML structure balanced in each file?
  • Are major tags balanced?
  • Are local paths correct?
  • Are anchors correct?
  • Does the slide navigation script work?
  • Are there any obvious rendering issues?

12. Final Quality Gate

  • Does the module feel like high-quality professional course material?
  • Does it avoid shallow summary-style writing?
  • Does it avoid generic AI boilerplate?
  • Does it feel memorable and teachable?
  • Does it support both self-study and live instruction?
  • Would a serious aspiring AI engineer find this genuinely useful?