{"slug": "from-documents-to-intelligent-answers-building-a-rag-agent-from-scratch-lessons", "title": "From Documents to Intelligent Answers: Building a RAG Agent from Scratch & Lessons Learned", "summary": "A developer built a Retrieval-Augmented Generation (RAG) system from scratch using Python, LangChain, Ollama, and Chroma. The project demonstrates how to combine document retrieval with LLM generation to answer questions based on private knowledge, reducing hallucination risks. Key lessons include the importance of chunking strategy, retrieval accuracy, and prompt design for enterprise AI assistants.", "body_md": "Artificial Intelligence applications are rapidly moving beyond simple question-answering systems. Modern enterprise AI assistants need to understand internal documents, retrieve accurate information, and provide reliable answers based on company knowledge.\n\nI started building agentic AI systems from scratch — not to theorize about them, but to actually write the code, break it, and understand it from the inside out. This post walks through the second project in that journey: a Retrieval-Augmented Generation (RAG) system, what I built, what tripped me up, and where I'm taking it next.\n\nRetrieval-Augmented Generation combines two capabilities:\n\nRetrieval: The system searches a knowledge base and finds relevant information related to the user's question.\n\nGeneration: The retrieved information is provided as context to an LLM, which generates a response based on that knowledge.\n\nInstead of asking an LLM to remember everything, RAG allows the model to access external knowledge dynamically.\n\nTechnologies Used\n\nPython, LangChain, Ollama (Local LLM), Embeddings, Vector Database, FastAPI.\n\nThe core idea is to build a system that answers questions using only the content of a document, rather than whatever the underlying model already \"knows.\"\n\nDocuments → Text Splitter → Embeddings → Vector Store → Retriever → LLM → Answer\n\nDocument loading — a company policy document, loaded with LangChain's TextLoader\n\nText Splitting — split into 200-character chunks with 50-character overlap, using RecursiveCharacterTextSplitter\n\nCreate Embeddings — generated with sentence-transformers/all-MiniLM-L6-v2 via HuggingFaceEmbeddings\n\nVector store — persisted in Chroma\n\nRetrieval — top-2 most relevant chunks pulled per question\n\nGeneration — a strict prompt template that instructs the model to answer only from the retrieved context, run through Ollama's tinyllama.\n\nBuilding a RAG system taught me that successful AI applications are not only about selecting a powerful LLM. The quality of the final answer depends heavily on:\n\nDocument quality\n\nChunking strategy\n\nRetrieval accuracy\n\nPrompt design\n\nEvaluation methods\n\nRAG provides a practical foundation for building enterprise AI assistants that can use private knowledge while reducing hallucination risks. This project became the foundation for my next experiments with multi-agent workflows, MCP servers, and autonomous AI systems.\n\nSource Code: [https://github.com/srirdeevi/agentic-ai-portfolio](https://github.com/srirdeevi/agentic-ai-portfolio)", "url": "https://wpnews.pro/news/from-documents-to-intelligent-answers-building-a-rag-agent-from-scratch-lessons", "canonical_source": "https://dev.to/sri_d_6dfd4d31319a6389eaa/from-documents-to-intelligent-answers-building-a-rag-agent-from-scratch-lessons-learned-4e4i", "published_at": "2026-07-14 20:16:45+00:00", "updated_at": "2026-07-14 20:29:48.206767+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "machine-learning", "developer-tools"], "entities": ["LangChain", "Ollama", "Chroma", "FastAPI", "HuggingFace", "tinyllama", "sentence-transformers/all-MiniLM-L6-v2"], "alternates": {"html": "https://wpnews.pro/news/from-documents-to-intelligent-answers-building-a-rag-agent-from-scratch-lessons", "markdown": "https://wpnews.pro/news/from-documents-to-intelligent-answers-building-a-rag-agent-from-scratch-lessons.md", "text": "https://wpnews.pro/news/from-documents-to-intelligent-answers-building-a-rag-agent-from-scratch-lessons.txt", "jsonld": "https://wpnews.pro/news/from-documents-to-intelligent-answers-building-a-rag-agent-from-scratch-lessons.jsonld"}}