what is Chain of thought? Why it is important for users who want to learn more with DeepSeek like LLS？

视频信息

答案文本 复制

视频字幕 复制

Chain of Thought is a reasoning method where AI models break down complex problems into step-by-step logical sequences. Instead of providing direct answers, Chain of Thought shows the thinking process. For example, when calculating 23 times 47, traditional AI might simply give 1081. But with Chain of Thought, we see each step: breaking 47 into 40 plus 7, applying distribution, calculating 920 plus 161, and reaching the final answer. This transparency makes the reasoning process visible and verifiable. Chain of Thought works by having language models break down complex problems into manageable steps. The process involves identifying key information, applying relevant formulas or logic, and building conclusions step by step. For example, with a word problem about train travel, the model first identifies the given values like speed and time, then applies the distance formula, and finally calculates the result. Each step builds upon the previous one, creating a clear reasoning chain that users can follow and verify. Chain of Thought provides significant benefits for learners. First, it improves transparency by making the reasoning process visible, unlike traditional black-box approaches. Second, it enables better error identification, allowing learners to spot mistakes in logic at specific steps. Third, it enhances understanding by teaching problem-solving methods rather than just providing answers. Finally, it increases confidence by letting learners verify each solution step. Think of it like building blocks or climbing a ladder - each step builds upon the previous one, creating a solid foundation for learning. DeepSeek implements Chain of Thought through advanced reasoning capabilities that make the thinking process transparent. When you submit a prompt, DeepSeek processes it through multiple stages: analyzing the problem type, breaking it into components, applying relevant methods, and verifying solutions. This works across various domains including mathematics, logic puzzles, scientific reasoning, and code analysis. The model shows its reasoning pipeline, allowing users to understand not just the final answer, but how it arrived at that conclusion through systematic thinking. To maximize learning with Chain of Thought, use effective strategies and prompting techniques. Request step-by-step explanations by asking DeepSeek to 'show your reasoning' or 'break down the problem.' Follow the reasoning chains carefully, identify any logic errors, and build upon the explanations for deeper understanding. Use prompts like 'Please solve this step by step and show your reasoning.' The learning process becomes a cycle: ask for detailed explanations, follow the reasoning steps, verify the logic, and learn from the process. This approach transforms AI interactions from simple question-answer sessions into comprehensive learning experiences.