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#24 Significantly advancing LLMs with RAG (Google's Gemini 2.0, Deep Research, notebookLM)
Manage episode 460336033 series 3585389
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Dev and Doc - Latest News
Dev and Doc - Latest News
It's 2025, Dev and Doc cover the latest news including Google's deep research and notebook LM, DeepMind's Promptbreeder, and Anthropic's new RAG approach. We also go through what retrieval augmented generation (RAG) is, and how this technique is advancing LLM performance.
👋 Hey! If you are enjoying our conversations, reach out, share your thoughts and journey with us. Don't forget to subscribe whilst you're here :)
Meet the Team
Where to Follow Us
Contact Us
📧 For enquiries - [email protected]
Credits
- 🎞️ Editor - Dragan Kraljević - Instagram
- 🎨 Brand Design and Art Direction - Ana Grigorovici - Behance
Episode Timeline
- 00:00 Highlights
- 00:53 News - Notebook LM, OpenAI 12 days of Christmas
- 07:44 Change in the meta - post-training
- 11:34 Optimizing prompts with DeepMind Promptbreeder
- 13:20 Is OpenAI losing their lead against Google
- 16:45 Deep research vs Perplexity
- 24:18 AIME and oncology
- 26:00 Deep research results
- 30:20 RAG intro
- 33:14 Second pass RAG
- 36:20 RAG didn't take off
- 38:40 Wikichat
- 39:16 How do we improve on RAG?
- 41:11 Semantic/topic chunking, cross-encoders, agentic RAG
- 51:15 Google’s Problem Decomposition
- 53:32 Anthropic’s Contextual Retrieval Processing
- 56:07 Summary and wrap up
References
31 פרקים
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