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Build a Custom RAG Pipeline from Scratch: A Step-by-Step Tutorial
1. Understanding the RAG Architecture & When to Use It
- Break down the core components: ingestion, embedding, vector storage, and retrieval-augmented generation.
- Compare RAG vs. fine-tuning — choose the right approach for your use case.
- Outline the data flow: raw documents → chunks → vectors → LLM response.
2. Setting Up Your Environment & Dependencies
- Install Python, LangChain, FAISS, and an embedding model (e.g., OpenAI or Sentence-Transformers).
- Configure a local LLM (
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