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Hosted by Sheetal ’Shay’ Dhar · 🇺🇸 US · EN · 74 episodes
Established thought leaders with verified media credentials.
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! Using vivid analogies and storytelling, I guide you through intricate ideas, helping you create mental images that stick. Whether you’re a tech enthusiast, business leader, technologist or just curious, my episodes bridge the gap between cutting-edge AI and everyday understanding. Dive in and let your imagination bring these concepts to life!
Sheetal ’Shay’ Dhar hosts The AI Concepts Podcast, a technology show with 74 episodes published.
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
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
This episode closes out Module 6 by tackling the question that has been getting louder since large context windows arrived. If a model can hold hundreds of thousands or even millions of tokens at once, do we still need a
This episode addresses the category of questions that vector search fundamentally cannot answer, questions about relationships between things. We explore what a knowledge graph is and why traversing connections between e
This episode addresses a retrieval failure that has nothing to do with your index and everything to do with the query itself. We explore the vocabulary gap between how people ask questions and how documents are written,
This episode addresses the fundamental tension between retrieval precision and generation context. We explore why small chunks produce tight embeddings that retrieve well but leave the model without enough surrounding in
This episode addresses the gap between finding candidate chunks and finding the right ones. We explore the bi-encoder bottleneck, why compressing text into a single vector for comparison loses critical nuance, and how cr
This episode addresses one of the most common gaps in RAG pipelines, relying solely on semantic search. We explore how dense retrieval works and where it excels, then introduce sparse retrieval with BM25 and why it catch
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
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 s
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 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
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, offli
This episode kicks off Module 6 with RAG (Retrieval Augmented Generation), the #1 architecture every serious enterprise actually uses. Discover why regular LLMs hallucinate on your private data and high-stakes queries, a
This episode covers reasoning models, the shift from manually guiding a model's thinking to letting the model reason through complex problems on its own before responding. It explains the concept of test-time compute, wh
This episode covers structured output, how you get a model to respond in predictable, machine-readable formats like JSON instead of natural language paragraphs. It walks through three approaches, from simply asking in th
This episode covers system prompts, the invisible instruction layer that shapes every model interaction before the user says a word. It explains the three-role message format, why the model is trained to treat system ins
This episode covers chain of thought prompting, how asking a model to show its reasoning makes it measurably better at complex tasks, and why that works at a mechanical level. It walks through manual and zero-shot chain
This episode explores in-context learning, the idea that you can dramatically change how a model behaves just by showing it examples inside the prompt, without changing a single weight. It walks through zero-shot, one-sh
This episode explores the hidden layer between your prompt and the model’s response: decoding and sampling. We look at how the model moves from a field of possible next tokens to the one it actually chooses, why the same
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The AI Concepts Podcast is hosted by Sheetal ’Shay’ Dhar. The show is categorised under technology (education) and has published 74 episodes.
The AI Concepts Podcast has published 74 episodes.
The AI Concepts Podcast regularly covers technology, education, courses. It sits in the technology category, with a education focus.
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