Dive into the latest episode as we delve into the transformative world of advanced prompt engineering techniques. Explore the dynamic functionalities of Retrieval-Augmented Generation (RAG) in the AI pipeline, providing a fresh perspective on integrating vast databases for real-time information retrieval and response generation. We'll also discuss the pioneering methods like Toolformer and ART: Automatic multi-step reasoning and tool-use for large language models, which are setting new standards in how AI performs complex tasks and reasoning.
Main Topics:

Retrieval-Augmented Generation (RAG): An in-depth look at how RAG is revolutionizing AI's ability to access and utilize information from large datasets to enhance decision-making and interactions.

Toolformer: Explore this cutting-edge approach in enhancing AI’s capabilities to use external tools autonomously for solving complex problems.

ART Methods: Discover how ART methods empower large language models to perform multi-step reasoning and interact with various tools, pushing the boundaries of what AI can achieve.


Tune in to understand how these advanced techniques are shaping the future of technology!
Tags: #AI #MachineLearning #TechInnovation #PromptEngineering #AdvancedAI

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https://podcasters.spotify.com/pod/show/saugatach/episodes/Ep-22-How-small-LLMs-are-outperforming-GPT3-using-a-Mixture-of-Experts-e2i5h3h

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References for main topic:

[2210.03493] Automatic Chain of Thought Prompting in Large Language Models

[2302.04761] Toolformer: Language Models Can Teach Themselves to Use Tools

[2303.09014] ART: Automatic multi-step reasoning and tool-use for large language models

GitHub - bhargaviparanjape/language-programmes

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