Chain of Thought (CoT)
A prompting technique where a model is instructed to articulate its step-by-step reasoning process before arriving at a final answer.
What this means for trainers
Writing pristine chain-of-thought demonstrations is one of the most lucrative tasks available to AI trainers, especially those with STEM backgrounds. You are literally generating the logical blueprints that teach the model how to think, structure an argument, and verify its own work. When reviewing a model's reasoning, use the 'find the first break' method: read the chain step by step and locate the exact point where the logic first diverges from a valid path. Once you find that point, treat everything downstream as tainted, even if the final number or conclusion happens to coincide with the correct answer. A response can be right for the wrong reasons, and rewarding that teaches the model that sloppy reasoning is acceptable as long as it gets lucky. The flip side matters just as much: when a model's reasoning diverges from your reference answer, don't assume the model is wrong. Golden answers are themselves written by humans and can contain their own errors. Before marking a divergence as a failure, verify independently whether the model's path is actually invalid, or whether it found a different, equally valid route (or caught a mistake in the reference solution).
Chain of Thought (CoT) is an advanced prompting and training technique where a model is instructed to explicitly articulate its step-by-step reasoning process before arriving at a final answer. Instead of asking a model to instantly jump to the solution of a complex math problem or logic puzzle, the prompt simply includes the directive to 'think step-by-step'.
This technique dramatically increases the accuracy of large language models on tasks requiring formal logic, mathematics, coding, and multi-step planning. By generating intermediate reasoning steps as physical tokens, the model effectively grants itself more 'compute time' to process the problem. Because it predicts the next token based on all previous tokens, writing out a logical deduction helps condition the model to output the correct final conclusion.
Historically, CoT was a prompt engineering trick. However, advanced frontier models (like OpenAI's o1 series) now have Chain of Thought built directly into their architecture through specialized reinforcement learning. These models generate hidden reasoning tokens before outputting text to the user, allowing them to self-correct, plan ahead, and backtrack on flawed logic internally.
Evaluating and generating CoT data is highly specialized. Models often make subtle logical leaps or calculation errors mid-thought, leading to a perfectly formatted but entirely incorrect final answer.
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