#15 Reasoning Techniques in Agentic AI: CoT, ToT, Self-Consistency & Debate | CoT and ToT скачать в хорошем качестве

#15 Reasoning Techniques in Agentic AI: CoT, ToT, Self-Consistency & Debate | CoT and ToT

How do modern AI agents reason, explore possibilities, and reduce hallucinations? In this video, we break down the most important Reasoning Techniques used in Agentic AI systems, including: ✅ Chain-of-Thought (CoT) – step-by-step reasoning ✅ Tree-of-Thought (ToT) – exploring multiple reasoning paths ✅ Self-Consistency – improving accuracy through multiple reasoning samples ✅ Multi-Agent Debate – agents arguing and critiquing to reach better answers You’ll learn how these reasoning techniques fit into real-world Agentic Design Patterns, when to use each one, and how they help build more reliable, explainable, and intelligent LLM-based systems. 🧠 What You’ll Learn Why reasoning is critical for modern LLM agents Differences between CoT, ToT, Self-Consistency, and Debate How reasoning agents reduce hallucinations Practical use cases in RAG, planning agents, and autonomous systems Design patterns used in production-grade Agentic AI 👨‍💻 Who This Video Is For AI / ML Engineers LLM & Agentic AI Builders Researchers & Students Anyone building RAG, autonomous agents, or AI copilots #AIReasoning #AIThinking #LLMArchitecture #ArtificialIntelligence #MachineLearning #DeepLearning #AgenticAI #LLMReasoning #ChainOfThought #TreeOfThought #SelfConsistency #MultiAgentDebate #ReasoningAgents #LLMAgents #AIArchitecture #RAG #AutonomousAgents #AIEngineering #AIExplained