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The AI Concepts Podcast

The AI Concepts Podcast

Written by: Sheetal ’Shay’ Dhar
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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!Copyright 2024 All rights reserved. Education Science
Episodes
  • Module 6: RAG | Chunking - Where You Cut Decides What Gets Found

    Module 6: RAG | Chunking - Where You Cut Decides What Gets Found
    Apr 29 2026

    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.
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    11 mins
  • Module 6: RAG | Data Ingestion - Before Your Documents Can Be Found

    Module 6: RAG | Data Ingestion - Before Your Documents Can Be Found
    Apr 27 2026

    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.
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    12 mins
  • Module 6: RAG | Vector Databases - Where That Meaning Gets Stored

    Module 6: RAG | Vector Databases - Where That Meaning Gets Stored
    Apr 27 2026

    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.
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    10 mins
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