#24 Significantly advancing LLMs with RAG (Google's Gemini 2.0, Deep Research, notebookLM)

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.

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Meet the Team

• 👨🏻‍⚕️ Doc - Dr. Joshua Au Yeung - LinkedIn
• 🤖 Dev - Zeljko Kraljevic - Twitter

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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

• Cross Encoders
• Wikichat
• Google's Problem Decomposition
• Anthropic's Contextual Retrieval
• Google AIME in Oncology
• DeepMind's Promptbreeder