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In this video, I dive into Google’s recent white paper on building agentic systems. Learn about the components of AI agents, the differences between agents and models, and key frameworks like React, Chain of Thought, and Tree of Thought. I also explain the importance of tools such as extensions, functions, and data stores, and how retrieval-augmented generation (RAG) is used to expand AI knowledge.
LINKS:
White paper: https://tinyurl.com/mr4yvhy4
Anthropic Blog: https://www.anthropic.com/research/building-effective-agents
Agents Video: https://youtu.be/icRKf_Mvmt8
💻 RAG Beyond Basics Course:
https://prompt-s-site.thinkific.com/courses/rag
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00:00 Google’s White Paper on Agentic Systems
00:24 Defining AI Agents
00:54 Components of an AI Agent
01:40 When to Use Agents vs. Workflows
02:38 Differences Between Agents and Models
04:32 Reasoning Frameworks for Agents
08:03 Tools for Enhancing Agent Capabilities
15:05 Enhancing Model Performance with Targeted Learning
All Interesting Videos:
Everything LangChain: https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr
Everything LLM: https://youtube.com/playlist?list=PLVEEucA9MYhNF5-zeb4Iw2Nl1OKTH-Txw
Everything Midjourney: https://youtube.com/playlist?list=PLVEEucA9MYhMdrdHZtFeEebl20LPkaSmw
AI Image Generation: https://youtube.com/playlist?list=PLVEEucA9MYhPVgYazU5hx6emMXtargd4z Read More Prompt Engineering
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