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Advanced RAG & Memory Integration (Chapter 19)

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Manage episode 523939527 series 3705593
Content provided by Keith Bourne. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Keith Bourne or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Unlock how AI is evolving beyond static models into adaptive experts with integrated memories. In the previous 3 episodes, we secretly built up what amounts to a 4-part series on agentic memory. This is the final piece of that 4-part series that pulls it ALL together.

In this episode, we unpack Chapter 19 of Keith Bourne's 'Unlocking Data with Generative AI and RAG,' exploring how advanced Retrieval-Augmented Generation (RAG) leverages episodic, semantic, and procedural memory types to create continuously learning AI agents that drive business value.

This also concludes our book series, highlighting ALL of the chapters of the 2nd edition of "Unlocking Data with Generative AI and RAG" by Keith Bourne. If you want to dive even deeper into these topics and even try out extensive code labs, search for 'Keith Bourne' on Amazon and grab the 2nd edition today!

In this episode:

- What advanced RAG with complete memory integration means for AI strategy

- The role of LangMem and the CoALA Agent Framework in adaptive learning

- Comparing learning algorithms: prompt_memory, gradient, and metaprompt

- Real-world applications across finance, healthcare, education, and customer service

- Key risks and challenges in deploying continuously learning AI

- Practical leadership advice for scaling and monitoring adaptive AI systems

Key tools & technologies mentioned:

- LangMem memory management system

- CoALA Agent Framework

- Learning algorithms: prompt_memory, gradient, metaprompt

Timestamps:

0:00 – Introduction and episode overview

2:15 – The promise of advanced RAG with memory integration

5:30 – Why continuous learning matters now

8:00 – Core architecture: Episodic, Semantic, Procedural memories

11:00 – Learning algorithms head-to-head

14:00 – Under the hood: How memories and feedback loops work

16:30 – Real-world use cases and business impact

18:30 – Risks, challenges, and leadership considerations

20:00 – Closing thoughts and next steps

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Visit Memriq.ai for AI insights, guides, and tools

Thanks for tuning in to Memriq Inference Digest - Leadership Edition.

  continue reading

22 episodes

Artwork
iconShare
 
Manage episode 523939527 series 3705593
Content provided by Keith Bourne. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Keith Bourne or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Unlock how AI is evolving beyond static models into adaptive experts with integrated memories. In the previous 3 episodes, we secretly built up what amounts to a 4-part series on agentic memory. This is the final piece of that 4-part series that pulls it ALL together.

In this episode, we unpack Chapter 19 of Keith Bourne's 'Unlocking Data with Generative AI and RAG,' exploring how advanced Retrieval-Augmented Generation (RAG) leverages episodic, semantic, and procedural memory types to create continuously learning AI agents that drive business value.

This also concludes our book series, highlighting ALL of the chapters of the 2nd edition of "Unlocking Data with Generative AI and RAG" by Keith Bourne. If you want to dive even deeper into these topics and even try out extensive code labs, search for 'Keith Bourne' on Amazon and grab the 2nd edition today!

In this episode:

- What advanced RAG with complete memory integration means for AI strategy

- The role of LangMem and the CoALA Agent Framework in adaptive learning

- Comparing learning algorithms: prompt_memory, gradient, and metaprompt

- Real-world applications across finance, healthcare, education, and customer service

- Key risks and challenges in deploying continuously learning AI

- Practical leadership advice for scaling and monitoring adaptive AI systems

Key tools & technologies mentioned:

- LangMem memory management system

- CoALA Agent Framework

- Learning algorithms: prompt_memory, gradient, metaprompt

Timestamps:

0:00 – Introduction and episode overview

2:15 – The promise of advanced RAG with memory integration

5:30 – Why continuous learning matters now

8:00 – Core architecture: Episodic, Semantic, Procedural memories

11:00 – Learning algorithms head-to-head

14:00 – Under the hood: How memories and feedback loops work

16:30 – Real-world use cases and business impact

18:30 – Risks, challenges, and leadership considerations

20:00 – Closing thoughts and next steps

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Visit Memriq.ai for AI insights, guides, and tools

Thanks for tuning in to Memriq Inference Digest - Leadership Edition.

  continue reading

22 episodes

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