Retrieval-Augmented Generation (RAG) · TRACK 01/05
RAG Fundamentals
What RAG is, when to choose it, and your first working pipeline.
// THE TRACK
01 · START HERERetrieval-Augmented GenerationAfter reading, you'll understand exactly what retrieval-augmented generation is, the problem it solves, and why most AI products are built on it.BEGINNERIngest, Retrieve, GenerateYou'll be able to trace a question through every stage of a RAG pipeline — ingestion, retrieval, and generation — and know what each stage contributes.BEGINNERRAG vs Fine-TuningYou'll know a clear decision rule for choosing between RAG and fine-tuning for your use case, plus the cases where you need both.BEGINNERBuild Your First RAG AppYou'll have built a minimal end-to-end RAG application and understand what every line of it is doing.INTERMEDIATERAG vs Long ContextYou'll understand the real cost, latency, and accuracy trade-offs between retrieval and just stuffing everything into a long context window.INTERMEDIATERAG LimitationsYou'll be able to spot the situations where RAG adds complexity without value, and know the simpler alternatives that work instead.INTERMEDIATERAG Still HallucinatesYou'll understand the specific reasons a RAG system still invents facts and the concrete fix for each cause.INTERMEDIATERAG vs Search EngineYou'll see exactly where RAG overlaps with a classic search engine and where it goes further by generating an answer.BEGINNERRAG Cost and LatencyYou'll learn which stages of a RAG pipeline cost the most money and time, and the simplest levers to reduce both.INTERMEDIATERAGFlowYou will understand what RAGFlow is, how its deep document understanding and agentic layer fit together, and where an all-in-one RAG engine fits in your stack.INTERMEDIATEtxtaiYou will understand what txtai is, how it combines an embeddings database, semantic search, and LLM workflows in one package, and when its all-in-one design helps.INTERMEDIATE