Implementing Reasoning Techniques: Chain of Thought, ReAct, and Self-Ask

Ready Tensor · Beginner ·🏗️ Systems Design & Architecture ·2mo ago
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…
Watch on YouTube ↗ (saves to browser)

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
The Cloudflare Outage EXPLAINED
Next Up
The Cloudflare Outage EXPLAINED
Coding with Lewis