Chain-of-Thought & Reasoning Techniques

Chain-of-thought (CoT) prompting is a technique where you ask the model to show its working rather than jumping straight to an answer. Simply adding "think step by step" to a prompt can dramatically improve performance on reasoning tasks - maths problems, logical puzzles, and complex analysis. The model breaks the problem into intermediate steps, and each step's output informs the next, reducing errors that occur when the model tries to leap to a conclusion. Variants include tree-of-thought (exploring multiple reasoning paths and choosing the best), self-consistency (generating several chains of reasoning and taking the most common answer) and reflection (asking the model to critique its own reasoning and try again). These techniques reveal something interesting about how language models work: they reason more effectively when they can "think out loud" in their output tokens, because each generated token influences what comes next. For practical use, CoT is most valuable when accuracy matters - financial analysis, technical problem-solving, complex decision-making. The trade-off is cost and speed: reasoning chains use more tokens and take longer to generate. But for high-stakes tasks, the improved reliability is usually worth it.