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Module 1: The Latent Space & Manifolds | How Models Encode Meaning

12 min•13 december 2025

This episode is about the hidden space where generative models organize meaning. We move from raw data into a compressed representation that captures concepts rather than pixels or tokens, and we explore how models learn to navigate that space to create realistic outputs. Understanding this idea explains both the power of generative AI and why it sometimes fails in surprising ways.

Fler avsnitt av The AI Concepts Podcast

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

29 apr.•11 min

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

27 apr.•12 min

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

27 apr.•10 min

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

27 apr.•8 min

Module 6: The RAG Pipeline - End to End

25 apr.•13 min

Module 6: What is RAG and Why it Exists

25 apr.•9 min

Module 5: Reasoning Models

17 apr.•8 min

Module 5: Structured Output and the Language of Software

17 apr.•8 min

Module 5: System Prompts and the Invisible Rules

17 apr.•11 min

Module 5: Chain of Thought Prompting

17 apr.•7 min

The AI Concepts Podcast med Sheetal ’Shay’ Dhar finns tillgänglig på flera plattformar. Informationen på denna sida kommer från offentliga podd-flöden.