From Documents to Intelligent Answers: Building a RAG Agent from Scratch & Lessons Learned

Short summary

A hands-on tutorial for building a Retrieval-Augmented Generation (RAG) system from scratch using LangChain, Chroma, HuggingFace embeddings, and Ollama's tinyllama. The author walks through the full pipeline: document loading, text splitting, embeddings, vector storage, retrieval, and generation with a strict prompt template. Key lessons emphasize that answer quality depends on document quality, chunking strategy, retrieval accuracy, prompt design, and evaluation methods.

• •Step-by-step RAG build using LangChain, Chroma, HuggingFace, and Ollama
• •Pipeline: documents → text splitter → embeddings → vector store → retriever → LLM → answer
• •Key takeaway: answer quality depends on chunking, retrieval accuracy, and prompt design, not just model choice

Generated with AI, which can make mistakes.