AI Engineering Notes

Personal coursework repo for Data Science Academy's AI Engineering track: notes, worked examples, and reference implementations built while going through the course. It is not a shipped application — there is no build, lint, or test suite, just content organized so it can be searched and reused later.

Layout

• gen-ai-and-llms/ — the active course track: generative AI and LLMs. Each numbered subfolder is one module (transformer architectures, prompt engineering, fine-tuning/QLoRA, LangChain, vector databases, RAG, deployment, ...). Start with gen-ai-and-llms/README.md — it's a cookbook that walks through every module with a short concept summary, links to the notes/code, and where each idea originally comes from. gen-ai-and-llms/q&a.md has quick Q&A for review, grounded in and linked back to those same notes.
• computer-vision/ — placeholder for a future course track; empty for now.

Environment

• Python 3.11 (.python-version), dependencies managed per-track with uv (each track has its own pyproject.toml; lockfiles are intentionally gitignored here since this is a notes repo, not a distributable package).
• Install/sync deps from inside a track's folder: uv sync. Run a script or notebook kernel in that env: uv run <cmd> or uv run jupyter lab.
• API keys used by exercises live in a gitignored .env (see each track's own docs for which variables it expects).