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Contributing to AI Engineering Lab

Thanks for helping make AI engineering training better. This program is a living curriculum: AI moves fast, and so should it.

Ways to contribute

  • Fix a notebook: open an issue describing what broke and on which environment.
  • Update a fact: model names, API names, and pricing change constantly. If a section is stale, update it and add the doc URL to that file's Sources section.
  • Add a skill guide: new harness or tool? Add a page under reference/skills/ and link it from reference/skills/README.md.
  • Propose a new week or module: first check ROADMAP.md; propose additions via an issue before writing a lot of content.
  • Improve the tracker: the Excel workbook is generated by scripts/build_tracker.py from curriculum/manifest.json. Edit the manifest (not the .xlsx) and regenerate: python scripts/build_tracker.py.

Content standards

  1. Original writing. We do not reproduce third-party course material or other GitHub training repos. Synthesize concepts in your own words; cite official vendor documentation (Databricks, Microsoft, Google, Amazon, tool docs) in a Sources section with URLs.
  2. Every module has a use case. Content should tie back to the ZoroLogistics case study (or a clearly justified new case).
  3. Weeks are lessons, not pointers. Every week folder must meet the rich lesson standard in curriculum/_templates/week-template.md: a 2,000 to 3,500-word README with a problem statement, deep concepts (tables, diagrams, worked examples, pitfalls, glossary), a notebook walkthrough, a quiz, and graded exercises with hints.
  4. Notebooks must be runnable. Include the required pip install lines or a requirements cell, seed data generation inline, and mark cells that need an API key or GPU with a ⚠️ REQUIRES banner. Every notebook ends with a printed metric; every code cell must be syntactically valid Python (run scripts/check_notebooks.py).
  5. No secrets. Never commit API keys, tokens, or workspace URLs.
  6. Respect the schedule. The 24-week structure is defined in curriculum/manifest.json; keep week content inside its week folder.

Style

  • Markdown headers with numbered week content (## 1. Concepts, ## 2. Notebook, ## 3. Use case, ## 4. Exercises, ## 5. Sources).
  • Prefer tables for comparisons. Prefer Mermaid for diagrams.
  • Write for a learner who has never used the tool before, but keep it tight.

Pull request checklist

  • Notebook executed end-to-end (or clearly marked as requiring GPU/API)
  • Sources section lists every vendor doc consulted
  • curriculum/manifest.json updated if the program structure changed
  • No secrets, no third-party course content

Questions?

Open a GitHub discussion, or write info@zorost.com. The program is developed by Zorost Intelligence AI Lab: zorost.com.