generative-ai-for-beginners vs ai-engineering-from-scratch
Two of the top projects in Learning and cookbooks, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
generative-ai-for-beginners
21-lesson course on building generative AI apps in Python and TypeScript
ai-engineering-from-scratch
Hands-on curriculum for building AI systems from math to agents
Verdict
generative-ai-for-beginners ranks higher, 80 to 75. Of the 4 score parts we measure for both, generative-ai-for-beginners leads on 1, ai-engineering-from-scratch on 2 and 1 is even.
- Adoptiongenerative-ai-for-beginners leads by 57 points, 96 to 39: a wide gap.
- FreshnessEven at 100.
- Maintenanceai-engineering-from-scratch leads by 33 points, 95 to 62: a wide gap.
- Agent-readyai-engineering-from-scratch leads by 15 points, 45 to 30: a wide gap.
Pick generative-ai-for-beginners if you are
Developers new to building LLM apps who know basic Python or TypeScript
- Build lessons ship code in both Python and TypeScript where possible
- Covers RAG, function calling, agents, fine-tuning and security in one sequence
Pick ai-engineering-from-scratch if you are
Developers learning to build LLM, RAG and agent systems from first principles
- Lessons ship runnable code; the first ones need only python3 and no extra dependencies
- Three staged projects with local graders (project_test.py --strict) and reference solutions
| What we compare | generative-ai-for-beginners | ai-engineering-from-scratch |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 96, widely used | 39, known |
| Freshness | 100, active | 100, active |
| Maintenance | 62, fair | 95, healthy |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 121.3k | 68.4k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | None published | Oct 2026 |
| Language | Jupyter Notebook | Python |
| Docker | No | No |
| GPU | Not needed | Not stated |
| arm64 or Apple Silicon | Not stated | Not stated |
generative-ai-for-beginners
A Microsoft Cloud Advocates course of 21 lessons, each a README with a short video and code samples, split into "Learn" concept lessons and "Build" lessons. Topics include prompt engineering, text and chat apps, embeddings search, image generation, function calling, RAG, agents, fine-tuning, and small, Mistral and Meta models. Code targets Azure OpenAI, OpenAI API, Microsoft Foundry Models or Foundry Local.
Who it is for: Developers new to building LLM apps who know basic Python or TypeScript
Strengths
- Build lessons ship code in both Python and TypeScript where possible
- Covers RAG, function calling, agents, fine-tuning and security in one sequence
- Lessons are independent, so you can start at any number
- MIT license; translations into 50+ languages
Weaknesses
- Samples assume Azure OpenAI, OpenAI API or Foundry access, which may need paid accounts
- Several lessons list video as coming soon
- Cloning includes 50+ translations, so sparse checkout is needed for a small download
- GitHub Models lessons are tied to a service retiring end of July 2026
- no GPU
- Needs Azure OpenAI Service, OpenAI API, Microsoft Foundry Models, Foundry Local, GitHub account
- Models: Azure OpenAI, OpenAI, Microsoft Foundry Models, Foundry Local, Hugging Face open models
ai-engineering-from-scratch
A course of 528 lessons across 20 phases, covering math foundations, ML fundamentals, LLM engineering, agents, tools and protocols, and production infrastructure. Lessons are runnable code in Python, TypeScript, Rust and Julia, with three graded projects: retrieval evaluation, agent trace debugging and a tool-call firewall. It can be followed on the website, from a local clone, or through tutor skills installed into a coding agent.
Who it is for: Developers learning to build LLM, RAG and agent systems from first principles
Strengths
- Lessons ship runnable code; the first ones need only python3 and no extra dependencies
- Three staged projects with local graders (project_test.py --strict) and reference solutions
- Tutor skills install via npx skills and save progress to LEARNING.md
- Code in Python, TypeScript, Rust and Julia; README translated into 30+ languages
Weaknesses
- 528 lessons is a large scope; total study time is not stated in the visible README
- Project starters fail by design until you implement the stages
- Tutor skills need Node.js, npx and a skill-capable coding agent host
- GPU and RAM needs for later phases are not stated in the visible README
- Needs Python 3.10+, Node.js 22.18+ (TypeScript project), Rust (Tool Call Firewall project), npx (optional, tutor skills)
More in Learning and cookbooks
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.