RAG & Retrieval
Grounding models in your data.
Retrieval-augmented generation (RAG) grounds a language model in external data by retrieving relevant documents at query time and adding them to the prompt, reducing hallucination and keeping answers current.
13 episodes
- Hallucinations Are a Data Architecture Problem | Sudhir Hasbe, Neo4j
- Agent Memory: The Last Battleground in the AI Stack | Richmond Alake, Oracle
- Context Poisoning Is Killing Your AI Agents: How to Stop It
- How Intercom Cut $250K/Month by Ditching GPT for Qwen
- Beyond Transformers: How Liquid AI Is Rethinking LLM Architecture | Maxime Labonne
- Mastering Multi-Agent Systems | MongoDB’s Mikiko Chandrasekhar
- The AI Agent Trust Gap: Bridging Risk to Reliability | Elastic’s Philipp Krenn
- Your Key to AI Success is Hiding in Plain Sight | Cohesity's Greg Statton
- AI's Two Extremes – Foundations & The Frontier | Databricks’ Denny Lee
- Why Enterprises Need a Different Approach to AI Agents | Lyzr’s Siva Surendira
- Breaking the Language Barrier: Smartling's AI Translation Pipeline | Olga Beregovaya
- Information Symmetry: DevRev's Bet on AI-Driven Enterprise Decisions | Manoj Agarwal
- AI Infrastructure & the Evolution of RAG | Weaviate's Bob van Luijt
Guests on this topic
Sudhir HasbeRichmond AlakeMichel TricotFergal ReidMaxime LabonneMikiko ChandrasekharPhilipp KrennGreg StattonDenny LeeSiva SurendiraOlga BeregovayaManoj AgarwalYash ShethBob van Luijt