RAG (Retrieval-Augmented Generation) solves the core limitations of LLMs — training cutoffs, no private data access, and hallucinations — by retrieving relevant document chunks at query time and injecting them into the prompt. The post explains the full pipeline: chunking documents, converting text to vector embeddings, storing them in a vector database (ChromaDB), performing semantic similarity search, and augmenting the LLM prompt with retrieved context. A complete working Python implementation is built step-by-step using LangChain, Google Gemini API, and ChromaDB to create a conversational PDF chatbot. Common production pitfalls (bad chunking, irrelevant retrieval, stale data, latency) and advanced techniques (hybrid search, reranking, agentic RAG, graph RAG) are also covered.
Table of contents
What is RAG?Why Traditional LLMs FailHow RAG Works InternallyHow to Build a Real RAG ProjectThe Full Data FlowCommon RAG ProblemsAdvanced RAG ConceptsFinal ThoughtsSort: