Chain-of-Thought
An LLM prompting technique where the model shows its reasoning process step-by-step before providing a final answer.
Overview
Chain-of-thought (CoT) prompting encourages LLMs to break down complex problems into steps, showing intermediate reasoning. This can improve accuracy on complex tasks and makes the reasoning process transparent for evaluation.
Chain-of-Thought in Testing
When testing AI systems that use chain-of-thought reasoning, you can evaluate the quality of the reasoning itself, not just the final answer. This involves examining whether each step follows logically from the previous one, checking if the reasoning addresses all aspects of the problem, and verifying that conclusions are properly supported by the intermediate steps. Step verification means testing not just whether the final answer is correct, but whether the path taken to reach that answer makes sense and demonstrates sound reasoning.
When to Test Chain-of-Thought
Chain-of-thought testing becomes particularly valuable for complex problem-solving scenarios where the answer isn't immediately obvious. Mathematical problems, multi-step logical puzzles, and analytical tasks all benefit from explicit reasoning chains. When users need to verify AI outputs before taking action—such as in medical, legal, or financial contexts—visible reasoning helps build appropriate trust.
Logical reasoning tasks are another key application area. When the AI needs to draw conclusions from multiple premises, evaluate competing hypotheses, or work through conditional logic, chain-of-thought prompting makes the inference process visible and verifiable. This transparency helps you identify where logical errors occur and why certain conclusions were reached.
Testing Patterns
Reasoning transparency tests examine whether the AI can clearly articulate its thought process in a way humans can follow. These tests evaluate the clarity of each step, the logical connections between steps, and whether important considerations are explicitly addressed rather than left implicit. Good chain-of-thought reasoning should read like a colleague explaining their thinking, not like opaque black-box processing.
Verifying intermediate steps involves checking each part of the reasoning chain independently. Does step two actually follow from step one? Are there logical leaps that skip necessary reasoning? Do the individual steps make sense even when evaluated in isolation? This granular testing helps identify exactly where reasoning breaks down, making it much easier to improve system performance than simply knowing the final answer was wrong.
Benefits for Testing
Debuggability improves dramatically when reasoning is shown explicitly. Instead of puzzling over why an AI gave a particular answer, you can trace through its logic step by step to find exactly where it went wrong. This makes fixing problems much faster and more targeted—you can focus on the specific reasoning pattern that failed rather than having to guess at internal processes.
Trust and verification become possible when users can examine the reasoning behind answers. Rather than blindly accepting or rejecting AI outputs, people can evaluate the quality of reasoning and make informed decisions about whether to trust each specific conclusion. This is especially crucial in high-stakes applications where blind trust in AI systems would be inappropriate, but well-reasoned outputs can be valuable.
Best Practices
Use chain-of-thought prompting for complex problems that genuinely require multi-step reasoning, such as mathematical calculations, logical puzzles, or analytical tasks with multiple considerations. Transparency becomes essential when users need to verify reasoning before acting on recommendations, particularly in domains like healthcare, finance, or legal advice. For debugging, chain-of-thought helps you understand where errors occur in the reasoning process. Showing work also builds trust—users feel more confident when they can see and evaluate the thinking behind answers.
When testing chain-of-thought reasoning, verify each intermediate step rather than just checking the final answer. Ensure logical flow by confirming that steps connect properly and follow from each other. The final answer should clearly follow from the reasoning shown, with no unexplained leaps. Evaluate clarity by asking whether the reasoning is easy to follow and understand—convoluted or unclear reasoning undermines the benefits of chain-of-thought even if the answer is correct.
To prompt for chain-of-thought reasoning effectively, make explicit requests like "Let's think step by step" or "Please show your reasoning." Provide format guidance such as "Show your work before providing the answer" to structure outputs appropriately. Including few-shot examples with good reasoning helps the model understand what kind of step-by-step thinking you're looking for, making it more likely to produce clear, logical chains of thought in its responses.