rohitg00 / ai-engineering-from-scratch
Learn it. Build it. Ship it for others.
description README.md
Implement model internals, retrieval pipelines, and agent runtimes. Test them, inspect failures, and keep the code and evaluation results.
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Free, open source, MIT. Learn on the website, with a coding agent, or by running local code.
523 lessons. 20 phases. Python, TypeScript, Rust, Julia.
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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.
python3 scripts/project_test.py retrieval-evaluation-lab \
--init learning-artifacts/retrieval-evaluation-lab
python3 scripts/project_test.py retrieval-evaluation-lab \
--stage 1 --path learning-artifacts/retrieval-evaluation-lab --strict
python3 scripts/project_test.py retrieval-evaluation-lab \
--all --path learning-artifacts/retrieval-evaluation-lab --strict
Keep: a reproducible compari
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