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المحتوى المقدم من Mark Smith [nz365guy]. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Mark Smith [nz365guy] أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
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How RAG Is Powering the Future of AI Agents

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Manage episode 499615167 series 2936583
المحتوى المقدم من Mark Smith [nz365guy]. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Mark Smith [nz365guy] أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM
🎙️ FULL SHOW NOTES
https://www.microsoftinnovationpodcast.com/717

What if your AI agent could not only retrieve facts but reason with them—just like a human? In this episode, Farzad Sunavala, Principal Product Manager at Microsoft, takes us inside the world of Retrieval-Augmented Generation (RAG), the architecture powering the next wave of intelligent agents. From solving hallucinations to building memory into AI systems, Farzad shares practical insights for professionals looking to build scalable, high-quality AI solutions that actually work in the real world.
🔑 KEY TAKEAWAYS
- RAG is foundational for AI agents: Retrieval-Augmented Generation solves key limitations of LLMs by grounding them in real-time, private data.
- Metadata is your best friend: Rich metadata and filtering techniques dramatically improve retrieval quality and reduce noise in enterprise AI systems.
- Memory is the next frontier: Embedding memory into agents—via tools like Semantic Kernel—enables learning, unlearning, and contextual recall.
- AI engineering is evolving fast: Developers must move beyond conventional software practices and embrace ML Ops, vector databases, and open-source frameworks.
- Start small, iterate smart: Building ground-truth datasets and synthetic Q&A pairs is a high-ROI strategy for evaluating and improving AI agent performance.

🧰 RESOURCES MENTIONED:
👉 Azure AI - https://ai.azure.com/
👉 Azure AI Searchhttps://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search or https://azure.microsoft.com/en-us/products/ai-services/ai-search/
👉 Microsoft Fabrichttps://www.microsoft.com/en-us/microsoft-fabric
👉 Semantic Kernel (Open Source)https://github.com/microsoft/semantic-kernel
👉 LangChainhttps://www.langchain.com/
👉 LlamaIndexhttps://www.llamaindex.ai/
👉 CrewAIhttps://www.crewai.com/

Support the show

If you want to get in touch with me, you can message me here on Linkedin.
Thanks for listening 🚀 - Mark Smith

  continue reading

فصول

1. How RAG Is Powering the Future of AI Agents (00:00:00)

2. From Oil Fields to AI Agents: Farzad’s Unlikely Journey (00:03:29)

3. Why RAG Is the Backbone of Enterprise AI Agents (00:04:39)

4. Fighting Human Error with Metadata and Smart Retrieval (00:09:10)

5. Building Memory into AI Agents: The Human Brain Blueprint (00:20:24)

6. The Future of AI Agents: Scaling Knowledge and Intelligence (00:27:53)

737 حلقات

Artwork
iconمشاركة
 
Manage episode 499615167 series 2936583
المحتوى المقدم من Mark Smith [nz365guy]. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Mark Smith [nz365guy] أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM
🎙️ FULL SHOW NOTES
https://www.microsoftinnovationpodcast.com/717

What if your AI agent could not only retrieve facts but reason with them—just like a human? In this episode, Farzad Sunavala, Principal Product Manager at Microsoft, takes us inside the world of Retrieval-Augmented Generation (RAG), the architecture powering the next wave of intelligent agents. From solving hallucinations to building memory into AI systems, Farzad shares practical insights for professionals looking to build scalable, high-quality AI solutions that actually work in the real world.
🔑 KEY TAKEAWAYS
- RAG is foundational for AI agents: Retrieval-Augmented Generation solves key limitations of LLMs by grounding them in real-time, private data.
- Metadata is your best friend: Rich metadata and filtering techniques dramatically improve retrieval quality and reduce noise in enterprise AI systems.
- Memory is the next frontier: Embedding memory into agents—via tools like Semantic Kernel—enables learning, unlearning, and contextual recall.
- AI engineering is evolving fast: Developers must move beyond conventional software practices and embrace ML Ops, vector databases, and open-source frameworks.
- Start small, iterate smart: Building ground-truth datasets and synthetic Q&A pairs is a high-ROI strategy for evaluating and improving AI agent performance.

🧰 RESOURCES MENTIONED:
👉 Azure AI - https://ai.azure.com/
👉 Azure AI Searchhttps://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search or https://azure.microsoft.com/en-us/products/ai-services/ai-search/
👉 Microsoft Fabrichttps://www.microsoft.com/en-us/microsoft-fabric
👉 Semantic Kernel (Open Source)https://github.com/microsoft/semantic-kernel
👉 LangChainhttps://www.langchain.com/
👉 LlamaIndexhttps://www.llamaindex.ai/
👉 CrewAIhttps://www.crewai.com/

Support the show

If you want to get in touch with me, you can message me here on Linkedin.
Thanks for listening 🚀 - Mark Smith

  continue reading

فصول

1. How RAG Is Powering the Future of AI Agents (00:00:00)

2. From Oil Fields to AI Agents: Farzad’s Unlikely Journey (00:03:29)

3. Why RAG Is the Backbone of Enterprise AI Agents (00:04:39)

4. Fighting Human Error with Metadata and Smart Retrieval (00:09:10)

5. Building Memory into AI Agents: The Human Brain Blueprint (00:20:24)

6. The Future of AI Agents: Scaling Knowledge and Intelligence (00:27:53)

737 حلقات

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