# RAG Fundamentals — Resources

## Official Docs

- [LangChain](https://docs.langchain.com) — RAG chains, retrievers, vector stores.
- [LlamaIndex](https://docs.llamaindex.ai) — Data frameworks, indexing, and RAG pipelines.
- [Pinecone Learning Center](https://www.pinecone.io/learn/) — Vector DB concepts and tutorials.

## Videos

- [3Blue1Brown — Neural Networks](https://www.youtube.com/watch?v=aircAruvnKk) — Foundation for understanding embeddings.
- [Andrej Karpathy — Embeddings](https://www.youtube.com/results?search_query=Andrej+Karpathy+embeddings) — Embedding intuition.
- [AI Explained — RAG](https://www.youtube.com/results?search_query=AI+Explained+RAG) — RAG overviews.
- [Fireship — RAG in 100 Seconds](https://www.youtube.com/results?search_query=Fireship+RAG) — Quick RAG intro.

## Articles

- [Lilian Weng — Retrieval-Augmented Generation](https://lilianweng.github.io/posts/2023-06-23-agent/) — RAG and agent patterns.
- [Chip Huyen — RAG](https://huyenchip.com/) — Production RAG considerations.
- [Simon Willison — RAG](https://simonwillison.net/series/rag/) — Practical RAG examples.

## Books

- **Build a Large Language Model (From Scratch)** by Sebastian Raschka — Embeddings and retrieval concepts.
- **AI Engineering** by Chip Huyen — RAG in production.

## Tools

- [LangChain](https://docs.langchain.com) — RAG framework.
- [LlamaIndex](https://docs.llamaindex.ai) — Data and RAG framework.
- [Pinecone](https://www.pinecone.io/) — Vector database.
- [Chroma](https://www.trychroma.com/) — Lightweight vector DB.
- [OpenAI Embeddings](https://platform.openai.com/docs/guides/embeddings) — Text embedding API.
- [Hugging Face Sentence Transformers](https://www.sbert.net/) — Open embedding models.