Chain of thought

Chain of Thought (CoT) is a concept in natural language processing (NLP) that enables language models to reason through tasks in a structured, step-by-step manner, similar to human thinking. Rather than generating isolated responses, a model using Chain of Thought builds on prior information, creating responses that follow a logical flow and ensuring a coherent, context-aware dialogue. This approach is particularly valuable in complex or multi-step queries, where understanding and maintaining context over multiple interactions is essential.

Imagine a healthcare chatbot asked, “What are the symptoms of diabetes, and how is it treated?” With Chain of Thought, the chatbot can sequence its response by first listing symptoms, then moving to treatment options, making the information easier to understand. Similarly, in customer support, a Chain of Thought approach allows a bot to acknowledge an issue, check the order status, and suggest solutions in a logical order.

Let’s see the Chain of Thought in Action!

From the agentic-workshop/lab-materials/02 folder, please open the notebook called 02.2-chain-of-thought.ipynb and follow the instructions.