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rohitg00 / ai-engineering-from-scratch

git clone https://github.com/rohitg00/ai-engineering-from-scratch.git v2026.10.4 · 1d ago 🔥 on GitHub Trending today

Learn AI Engineering! Learn it. Build it. Ship it for others.

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stars, 7 days
+17k
stars, 30 days · #13
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forks · 74 open issues

Star history starts 2026-10-20; the chart appears after the second daily snapshot.

LanguagePython
LicenseMIT
Created2026-03-18 (7mo ago)
Last push1d ago
Watchers450
OwnerUser

Seen on GitHub Trending

  • 2026-10-20 — #6 monthly in python · +16,662 ★
  • 2026-10-20 — #14 monthly · +16,662 ★

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README excerpt
Implement model internals, retrieval pipelines, and agent runtimes. Test them, inspect failures, and keep the code and evaluation results.
**Start learning** · **Choose a path** · **Try a lab** · **Build a project** · **Browse the curriculum**
Free, open source, MIT. Learn on the website, with a coding agent, or by running local code.
> 542 lessons. 20 phases. Python, TypeScript, Rust, Julia.
 Read in your language 
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### Sponsors
 Your support keeps every lesson free and open source. See all supporters · Become a sponsor 
## Learning paths
| Route | Starting lesson |
|---|---|
| Model foundations | Setup and tooling |
| LLM systems | Prompt engineering |
| Agents and delivery | The agent loop |
Compare career paths · Prerequisites and study time
### Gradient descent
Twenty starting points follow gradient descent on a quadratic loss. The graph shows their positions and mean loss after each update.
Adjust the learning rate in the lesson · Compare GD, momentum, and Adam in code
### Projects
Three projects with staged starters, reference implementations, and local graders. Run commands from the repository root after setup. Starters fail until you implement the stages.
 01 · Retrieval Evaluation Lab · Python · Ranking metrics and regression checks 
A candidate improves mean NDCG while one query ranks its most relevant evidence lower. Build a query-by-query comparison that reports the regression and can fail a release check.
Use Python 3.10+. Review RAG and model evaluation. Implement ranking validation, precision and recall, rank-sensitive metrics, then system comparison.
 [code] 
**Keep:** a reproducible comparison with per-query deltas and the judgments used to score them. Metrics describe those judgments; they do not establish answer correctness.
Start the project · Inspect the reference · Run on your own inputs
 02 · Agent Trace Debugger · TypeScript · Trace parsing and timing 
A supplied trace still takes 100 ms, but total token usage rises by 200 and one span starts failing. Separate overlapping child work from parent execution time, then produce a report that exposes the change.
Use Node.js 22.18+ and Python 3 for the grader. Implement JSONL parsing, parent validation, interval arithmetic, then an inspectable timeline.
 [code] 
**Keep:** the input trace, an HTML timeline, and a JSON regression report. Preserve exclusive per-span token counts so parent and child usage are not counted twice.
Start the project · Inspect the reference · Explore timing interactively
 03 · Tool Call Fi