generative-ai-for-beginners vs rag_techniques
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
rag_techniques
Collection of 42 runnable notebooks implementing RAG techniques
Verdict
generative-ai-for-beginners ranks higher, 81 to 79. Of the 4 score parts we measure for both, generative-ai-for-beginners leads on 1, rag_techniques on 2 and 1 is even.
- Adoptiongenerative-ai-for-beginners leads by 25 points, 98 to 73: a wide gap.
- FreshnessEven at 100.
- Maintenancerag_techniques leads by 13 points, 76 to 63.
- Agent-readyrag_techniques 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 rag_techniques if you are
Engineers learning and comparing RAG techniques through runnable code
- 42 notebooks grouped by category: foundational, query enhancement, context enrichment, and more
- Colab badge on listed notebooks, so no local setup is needed to run them
| What we compare | generative-ai-for-beginners | rag_techniques |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 98, widely used | 73, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 63, fair | 76, fair |
| Agent-ready | 30, minimal | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 122.7k | 30.2k |
| License | MIT (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | None published | Oct 2026 |
| Language | Jupyter Notebook | Jupyter Notebook |
| 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
rag_techniques
A repository of Jupyter notebooks, each implementing one RAG technique, from basic RAG and CSV RAG to HyDE, proposition chunking, contextual chunk headers, MemoRAG and RAG evaluation. Notebooks open in GitHub or Colab. It is a teaching resource with code and references, not a library or a deployable service.
Who it is for: Engineers learning and comparing RAG techniques through runnable code
Strengths
- 42 notebooks grouped by category: foundational, query enhancement, context enrichment, and more
- Colab badge on listed notebooks, so no local setup is needed to run them
- Covers chunking, query transformation, reliability and evaluation, with video explainers for some
- Recent additions include MemoRAG, Open-RAG-Eval and JSON RAG
Weaknesses
- Notebooks, not an installable package or service; code must be adapted for production
- License reported as NOASSERTION, so reuse terms are unclear
- README carries heavy sponsor, book and newsletter promotion
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