Embeddings & Retrieval-Augmented Generation (RAG): A Hands-On Learning Path

RAG (Retrieval-Augmented Generation) is a powerful technique that combines dense vector search with in-context learning. This guide curates the most actionable resources to help you understand and build effective RAG pipelines.

🔍 Core Topics Covered

1. What are embeddings and how are they used in vector search?
2. How to build and evaluate a RAG pipeline
3. Best practices for selecting and evaluating embedding models
4. Hybrid search, chunking, indexing, and query rewriting

📚 Recommended Resources

1. Microsoft Learn – Develop a RAG solution Deep dive into embedding generation, model selection, and quality metrics. https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/rag/rag-solution-design-and-evaluation-guide

2. Pinecone – What is Retrieval-Augmented Generation (RAG)? Excellent conceptual and practical introduction with end-to-end explanation. https://www.pinecone.io/learn/retrieval-augmented-generation/?utm_source=chatgpt.com

3. SingleStore – Beginner’s Guide to RAG A hands-on walkthrough for building your first RAG system. https://www.singlestore.com/blog/a-guide-to-retrieval-augmented-generation-rag/?utm_source=chatgpt.com

4. Smashing Magazine – A Simple Guide to Retrieval-Augmented Generation Focuses on indexing, chunking, and reducing hallucination in RAG applications. https://www.smashingmagazine.com/2024/01/guide-retrieval-augmented-generation-language-models/?utm_source=chatgpt.com

🛠️ Build a Project

• Create a basic Python RAG app that lets users ask questions about uploaded PDF files.
• Use LangChain with OpenAI embeddings and a vector store like Pinecone or Qdrant.
• Implement chunking, retrieval, and prompt assembly manually to understand the flow.

🧠 Reflect

• Can your retrieval system return the most relevant documents?
• How does changing the embedding model or chunking strategy affect output quality?