Implementing Reasoning Techniques: Chain of Thought, ReAct, and Self-Ask
In this video, we walk through how to implement advanced reasoning techniques in code for agentic AI systems. You’ll learn how to integrate Chain of Thought, ReAct, and Self-Ask into a modular prompt architecture, and why this design choice matters for scalability and reuse.
We start by reviewing the three reasoning strategies conceptually, then dive into a real codebase to show exactly how they’re wired into configuration files and prompt builders. You’ll see how a small, clean change can improve LLM response quality across all tasks in your system.
The second half of the video shares the r…
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Chapters (8)
Introduction and overview of reasoning in agentic AI
0:20
Reasoning strategies: Chain of Thought, ReAct, and Self-Ask
0:43
Config-driven reasoning strategies in code
1:27
Injecting reasoning into the prompt builder
2:09
Comparing outputs with and without Chain of Thought
2:50
Why modularity matters in real AI systems
3:35
A real-world story on reuse and wasted AI effort
5:12
The mission behind Ready Tensor
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