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 real-world motivation behind Ready Tensor — a story about reusable AI systems, modular design, and why so many AI solutions get rebuilt from scratch instead of reused. Timestamps: 0:00 - 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 Watch this video if you’re building agentic AI systems, designing modular prompt frameworks, or trying to move beyond ad-hoc prompt engineering toward production-ready architectures. This video is part of the Agentic AI Developer Certification Program by Ready Tensor. Enroll Now: https://www.readytensor.ai/agentic-ai-essentials-cert/ About Ready Tensor: Ready Tensor helps AI and ML professionals design, build, evaluate, and share real-world agentic AI systems through certifications, hands-on projects, and open, reusable AI publications. Learn more: https://www.readytensor.ai/ Like the video? Subscribe and let us know what other agentic AI design patterns you’d like us to cover.