Show Notes
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.
More from The AI Concepts Podcast
In this episode, we bring everything together by following one LLM application from the user's first request to the final response. We see retrieval, context, tools, model calls, state, memory and orchestration working as one system, revealing the bigger lesson of the module: building with LLMs is not just about what the model can do, but deciding what the model should do and what the rest of the application should handle.
In this episode, we map the modern LLM application stack and explore where frameworks, SDKs, runtimes and protocols actually fit. We look at how tools like LangChain, LangGraph, LlamaIndex, provider SDKs, durable workflows and MCP solve different problems, and why understanding the architecture matters more than memorizing product names.
This episode separates two concepts that are often confused: state, which keeps track of what is happening now, and memory, which allows information from the past to become useful later. We explore how applications create continuity around a model, and why good memory is not about remembering everything, but remembering the right things at the right time.
This episode breaks down what orchestration actually means, from sequencing and routing to retries, parallel execution and human approvals, and explores the different ways those flows can be managed. Most importantly, we separate orchestration from the model itself and show why it is really about controlling how work moves through an application.
Who actually decides what happens next inside an LLM application? This episode explores the difference between decisions made by code and decisions made by the model, and why real applications often use both. We follow the loop that emerges when a model can request information or actions, receive the results and decide what to do next, revealing what developers mean when they talk about “owning the loop” and setting the stage for orchestration.