#15 Reasoning Techniques in Agentic AI: CoT, ToT, Self-Consistency & Debate | CoT and ToT
Автор: Tech@AI-Info
Загружено: 2026-01-27
Просмотров: 12
Описание:
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
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