Chain-of-Thought: Make the Model Show Its Work

Chain-of-Thought

Part of: Prompt Engineering

Ask a model a multi-step word problem and demand only the final number, and it often blurts a wrong one. Ask the same model to "think step by step" first, and accuracy jumps. That single instruction is chain-of-thought prompting, and on multi-step math, logic, and planning tasks it is one of the highest-impact moves you have. What it is Chain-of-thought (CoT) prompting asks the model to lay out its intermediate reasoning before committing to an answer, instead of jumping straight to the result. The classic trigger is the phrase "Let's think step by step." You are not teaching the model new facts. You are giving it room to work the problem out in the open. - Direct answer : "What is 17 x 24? Answer with just the number." The model has to nail it in one shot. - Chain-of-thought : "What is 17 x 24? Think step by step, then give the final answer." Now it can write 17 x 24 = 17 x 20 + 17 x 4 = 340 + 68 = 408 and check itself along the way. Why it works Every token the model writes becomes part of the context it reads for the next token. When it writes out "340 + 68", those partial results are now sitting in the prompt, so the final token is predicted with the sub-steps already computed

Challenge: Verify the Chain of Thought