[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-chain-of-thought-prompting::en":3,"gloss-cluster-chain-of-thought-prompting::en":20,"gloss-next-chain-of-thought-prompting::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"chain-of-thought-prompting","prompt-eng","Chain-of-Thought Prompting","Chain-of-thought (CoT) prompting is a technique that instructs a large language model to work through a problem in explicit, sequential reasoning steps before producing its final answer, rather than jumping straight to a conclusion. Popularized by a 2022 Google research paper, CoT dramatically improves accuracy on tasks that require multi-step logic — arithmetic, multi-hop question answering, planning, and complex rule-following — because it gives the model a chance to \"show its work,\" catching errors it would otherwise make by pattern-matching directly to an answer. The simplest form is the zero-shot variant, triggered by appending a phrase like \"Let's think step by step\" to a prompt, which alone measurably improves accuracy on reasoning benchmarks. A more powerful variant, few-shot CoT, includes worked examples where the example outputs show the full reasoning chain, not just the final answer, teaching the model the expected reasoning style. For SaaS builders, CoT is especially valuable in features involving calculations, multi-condition business logic, or decisions that need to be auditable (a human reviewer can check the reasoning, not just trust a black-box answer). The trade-off is verbosity and cost: CoT responses are longer, consuming more output tokens and adding latency, so production systems often use CoT internally then extract just the final answer for display, or use a cheaper\u002Ffaster model for the final formatting step. Modern \"reasoning models\" (e.g., Claude's extended thinking mode, OpenAI's o-series) build CoT-style extended reasoning into the model's inference process itself, sometimes reducing the need to manually prompt for it, but explicit CoT prompting remains useful for controlling reasoning depth and structure even with reasoning-capable models. Concrete worked example: a SaaS pricing-quote tool prompt: \"A customer needs 47 licenses of our Pro plan ($29\u002Fuser\u002Fmonth), with a 15% annual discount and a $500 one-time setup fee. Think step by step to calculate the total first-year cost, then give the final answer on the last line as 'TOTAL: $X'.\" The model reasons: \"47 × $29 = $1,363\u002Fmonth → $16,356\u002Fyear. Apply 15% discount: $16,356 × 0.85 = $13,902.60. Add setup fee: $13,902.60 + $500 = $14,402.60.\" then outputs \"TOTAL: $14,402.60\" — the explicit steps make an arithmetic error far less likely than asking for the total directly, and let a developer log\u002Faudit the reasoning if the number ever looks wrong. One important production nuance: CoT reliably improves accuracy on genuinely multi-step reasoning tasks, but adding \"think step by step\" to a task that's actually simple (a lookup, a straightforward classification with an obvious answer) tends to add latency and cost without improving — and occasionally slightly hurting — accuracy, so mature prompt engineering applies CoT selectively based on task complexity rather than as a blanket default on every prompt.","Chain-of-thought prompting asks a model to reason step by step before giving a final answer, improving accuracy on complex tasks.",null,[11,14,17],{"slug":12,"name":13},"react-prompting","ReAct Prompting",{"slug":15,"name":16},"self-consistency","Self-Consistency",{"slug":18,"name":19},"tree-of-thought","Tree-of-Thought",[21,25,28,31,34,38,41,44,47,50,53,56],{"slug":22,"category":5,"name":23,"updated_at":24},"analogical-prompting","Analogical Prompting","2026-08-24T02:46:37+00:00",{"slug":26,"category":5,"name":27,"updated_at":24},"automatic-prompt-optimization","Automatic Prompt Optimization",{"slug":29,"category":5,"name":30,"updated_at":24},"chain-of-density","Chain of Density (CoD)",{"slug":32,"category":5,"name":33,"updated_at":24},"chain-of-verification","Chain-of-Verification",{"slug":35,"category":5,"name":36,"updated_at":37},"chunking","Chunking","2026-08-24T02:46:36+00:00",{"slug":39,"category":5,"name":40,"updated_at":37},"constrained-decoding","Constrained Decoding",{"slug":42,"category":5,"name":43,"updated_at":37},"context-stuffing","Context Stuffing",{"slug":45,"category":5,"name":46,"updated_at":37},"delimiter","Delimiter",{"slug":48,"category":5,"name":49,"updated_at":24},"directional-stimulus-prompting","Directional Stimulus Prompting",{"slug":51,"category":5,"name":52,"updated_at":24},"emotion-prompting","Emotion Prompting",{"slug":54,"category":5,"name":55,"updated_at":37},"few-shot-prompting","Few-Shot Prompting",{"slug":57,"category":5,"name":58,"updated_at":24},"generated-knowledge-prompting","Generated Knowledge Prompting"]