The AI Concepts Podcast

Module 6: RAG | Chunking - Where You Cut Decides What Gets Found

This episode is about chunking, the quiet step in a RAG pipeline that decides whether your system retrieves the right answer or a confidently wrong one. It covers why the chunk is the real unit of retrieval, the tradeoff between context and precision, the main strategies teams use to split documents, and why testing your chunks against real questions matters more than picking the perfect size.

Module 6: RAG | Data Ingestion - Before Your Documents Can Be Found

This episode is about the step that every RAG system depends on. Before meaning can be stored or retrieved, your raw documents have to become clean text. What goes wrong here breaks the entire pipeline in ways that are surprisingly hard to catch.

Module 6: RAG | Vector Databases - Where That Meaning Gets Stored

This episode is about the infrastructure underneath every RAG system. It covers the purpose-built engine that stores all that meaning and searches millions of vectors in milliseconds, in a way no traditional database can. This is what makes retrieval fast enough to actually work in production.

Module 6: RAG | Embeddings - Teaching Machines to Understand Meaning

This episode is about the layer of RAG that makes semantic search possible. It covers how machines turn language into math that clusters similar ideas together, so a question and its answer can find each other even when they share no words in common. Without this, RAG is just keyword search with extra steps.

Module 6: The RAG Pipeline - End to End

This episode maps out the full RAG pipeline end to end using one concrete scenario, a defense contractor building an AI assistant for fighter jet maintenance crews. It walks through both phases of the architecture, offline and online, following a real question all the way from a raw document to a grounded answer. It also covers why the architecture is modular and closes with the four failure modes that quietly break RAG systems in production.