Chain of thought(CoT) Prompting | Prompt Engineering : part 3

In this third Lecture of the Prompt Engineering course, we dive deeper into advanced prompting strategies with a focus on Chain of Thought (CoT) Prompting. This lecture explores how CoT enhances the logical reasoning capabilities of large language models by guiding them through step-by-step problem-solving. You will learn the fundamentals of CoT, its various approaches—Zero-Shot, Manual, Automatic, and Multimodal—and how to apply them effectively. With practical examples and real-world applications, this session demonstrates how CoT improves AI performance in tasks like mathematical reasoning, decision-making, and content creation, while also addressing its advantages and limitations. Join us to unlock the power of structured reasoning in AI. Timestamps: 0:00 - Introduction to Lecture 3 0:23 - Introduction to Chain of Thought (CoT) Prompting 0:46 - What is Chain of Thought Prompting? 1:36 - Origins of CoT: 2022 Research Paper 2:01 - How to Construct an Effective CoT Prompt 3:03 - How CoT Prompting Works 3:44 - Practical Example: Classroom Chair Ratio Problem 4:34 - Example: Project Management with CoT 4:55 - Comparison: Standard vs. Chain of Thought Prompting 6:29 - Approaches to CoT Prompting 6:52 - Zero-Shot CoT Prompting Explained 7:37 - Example: Calculating ROI with Zero-Shot CoT 8:18 - Advantages and Limitations of Zero-Shot CoT 9:12 - Manual CoT Prompting Explained 10:09 - Example: Structured Problem Solving with Manual CoT 10:45 - Advantages and Limitations of Manual CoT 11:40 - Automatic CoT (AutoCoT) Prompting Explained 13:20 - How AutoCoT Works: Visual Representation 14:20 - Benefits of AutoCoT 15:33 - Limitations of AutoCoT 16:16 - Multimodal CoT Prompting Explained 17:41 - Example: Magnets Attraction with Multimodal CoT 18:35 - Real-World Applications of CoT Prompting 19:39 - Application 1: Customer Service Chatbots 19:59 - Application 2: Research and Innovation 20:32 - Application 3: Content Creation and Summarization 20:59 - Application 4: Education and Learning 21:18 - Application 5: AI Ethics and Decision-Making 21:57 - Why CoT Prompting Matters: Key Advantages 23:29 - Limitations of CoT Prompting 24:12 - Tips and Tricks: Optimizing CoT Usage 24:51 - Enhancing CoT with Chain of Action Thought (CoAT) 26:05 - Chain of Draft (CoD): Efficient CoT Prompting 27:20 - Conclusion and Summary Here is the link for Git repository -https://github.com/cloudxlab Drop us a mail at [email protected] in case of any query. To know more about CloudxLab visit - https://cloudxlab.com/ Follow us on social media for the latest updates- Facebook- https://www.facebook.com/cloudxlab/ Instagram- https://www.instagram.com/cloudxlab Twitter- https://www.twitter.com/cloudxlab LinkedIn- https://www.linkedin.com/company/cloudxlab