The AI Concepts Podcast
The AI Concepts Podcast is my attempt to turn the complex world of artificial intelligence into bite-sized, easy-to-digest episodes. Imagine a space where you can pick any AI topic and immediately grasp it, like flipping through an Audio Lexicon - but even better!
- Indexed episodes, last 90 days
- 5
- Latest publication
- Sep 6, 2026
- Audience
- Checking…
- Earliest in this view
- Aug 19, 2026
Latest episodes
Module 7: Building LLM Applications | State and Memory: How Does an LLM Application Remember? (opens the original)
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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.
Module 7: Building LLM Applications | What Does Orchestration Actually Mean? (opens the original)
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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.
Module 7: The LLM Application Loop (opens the original)
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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.
Module 7: Why Do We Need LLM Frameworks? (opens the original)
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If LLM applications can be built with regular code and APIs, why do frameworks exist at all? This episode explores what happens as a simple application grows and starts needing retrieval, memory, routing, multiple models, retries and tracing. We look at what frameworks actually take off the developer’s plate, when those abstractions become useful, and why sometimes plain code is still the better choice.
Module 7: Building LLM Applications | What Is an LLM Application, Really? (opens the original)
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What actually sits behind an LLM application? This episode takes one simple request and follows it beneath the surface, revealing how the model, application code, APIs, external data and context work together to produce something genuinely useful. As the request gets more complex, we begin to see why concepts like memory, tools and orchestration enter the picture. It is a practical look at what we are really building when we say we are building with LLMs.
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