--- title: Chain of Thought Prompting, Explained So You Actually... description: Chain of thought (CoT) prompting is often misrepresented as a universal accuracy booster. The original Wei et al. (2022) result was specific: a 3x improvement... canonical: https://daily.dev/posts/chain-of-thought-prompting-explained-so-you-actually-use-it-right-2026--bhbe4ffyl twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Chain of Thought Prompting, Explained So You Actually Use It Right (2026) | daily.dev og:description: Chain of thought (CoT) prompting is often misrepresented as a universal accuracy booster. The original Wei et al. (2022) result was specific: a 3x improvement... og:url: https://daily.dev/posts/chain-of-thought-prompting-explained-so-you-actually-use-it-right-2026--bhbe4ffyl og:image: https://api.daily.dev/og/posts/BHBE4ffyl.png og:image:alt: Chain of Thought Prompting, Explained So You Actually Use It Right (2026) og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Chain of Thought Prompting, Explained So You Actually Use It Right (2026) **[Noor Mohammad](https://daily.dev/sources/ew9hy5zthevyyefagop4g)** · [@noormohammad](https://daily.dev/noormohammad) · 1 min read · 0 upvotes · 1 comments ## Summary Chain of thought (CoT) prompting is often misrepresented as a universal accuracy booster. The original Wei et al. (2022) result was specific: a 3x improvement on multi-step math problems using a 540B model — not a general-purpose gain. On simple tasks, CoT can hurt performance. In 2026, models like Claude and Gemini already reason internally by default, making explicit 'think step by step' prompts redundant or counterproductive. The post clarifies what CoT actually is, where it genuinely helps, and — critically — when to avoid it. ## Content "Adding chain of thought boosts accuracy by 61%" gets quoted like a law of physics. It isn't one. The real result (Wei et al., Google, Jan 2022): on a 540B model, step-by-step reasoning lifted grade-school math word problems from ~18% → ~57%. That's a 3x jump on ONE class of hard, multi-step problems on a very large model — not a universal buff. On simple tasks it does nothing, or hurts. And in 2026 it matters more: Claude and Gemini already reason by default, so bolting on "think step by step" is often redundant or actively degrades output. Master AI post 3 — what CoT really is, and when NOT to use it. 🔗 [https://medium.com/@reactjsbd/chain-of-thought-prompting-explained-so-you-actually-use-it-right-2026-e82ae367ddad?sharedUserId=reactjsbd](https://medium.com/@reactjsbd/chain-of-thought-prompting-explained-so-you-actually-use-it-right-2026-e82ae367ddad?sharedUserId=reactjsbd) ## Community discussion Top comments from developers on daily.dev. **@noormohammad** · 0 upvotes > The 2026 wrinkle is the useful part: on reasoning models (Claude, Gemini, o-series), does explicit "think step by step" still help you, or have you seen it hurt/no-op? Curious what people are measuring — especially on simple tasks where CoT can backfire. ## Similar posts on daily.dev - [A guide to chain of thought prompting](https://daily.dev/posts/a-guide-to-chain-of-thought-prompting-oqxbmzzke) · Serokell · 0 upvotes · 0 comments - [\[2211.12588\] Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://daily.dev/posts/2211-12588-program-of-thoughts-prompting-disentangling-computation-from-reasoning-for-numerical-r-bf0dak3ez) · Hacker News · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#prompt-engineering](https://daily.dev/tags/prompt-engineering) [View this post on daily.dev](https://daily.dev/posts/chain-of-thought-prompting-explained-so-you-actually-use-it-right-2026--bhbe4ffyl) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"DiscussionForumPosting","mainEntityOfPage":"https://daily.dev/posts/chain-of-thought-prompting-explained-so-you-actually-use-it-right-2026--bhbe4ffyl","headline":"Chain of Thought Prompting, Explained So You Actually Use It Right (2026)","text":"Chain of thought (CoT) prompting is often misrepresented as a universal accuracy booster. The original Wei et al. (2022) result was specific: a 3x improvement on multi-step math problems using a 540B model — not a general-purpose gain. On simple tasks, CoT can hurt performance. In 2026, models like Claude and Gemini already reason internally by default, making explicit 'think step by step' prompts redundant or counterproductive. The post clarifies what CoT actually is, where it genuinely helps, and — critically — when to avoid it.","url":"https://daily.dev/posts/chain-of-thought-prompting-explained-so-you-actually-use-it-right-2026--bhbe4ffyl","datePublished":"2026-07-22T08:48:17.822Z","dateModified":"2026-07-22T08:48:34.563Z","author":{"@type":"Person","name":"Noor Mohammad","url":"https://daily.dev/noormohammad","image":"https://media.daily.dev/image/upload/s--3w4n0n-6--/f_auto/v1754054115/avatars/avatar_eW9hy5zTHEVyYeFaGOP4G?_a=BAMClqZW0","description":"Software Engineer | React, Next.js & scalable web apps | Writing on AI, blockchain & emerging tech","interactionStatistic":{"@type":"InteractionCounter","interactionType":{"@type":"EndorseAction"},"userInteractionCount":120}},"image":"https://media.daily.dev/image/upload/s--1gKQokXe--/f_auto/v1784710098/posts/BHBE4ffyl?_a=BAMAMicg0","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":1}],"comment":[{"@type":"Comment","text":"The 2026 wrinkle is the useful part: on reasoning models (Claude, Gemini, o-series), does explicit “think step by step” still help you, or have you seen it hurt/no-op? Curious what people are measuring — especially on simple tasks where CoT can backfire.","datePublished":"2026-07-22T08:48:30.272Z","url":"https://daily.dev/posts/BHBE4ffyl#c-ikzFAVTIh","author":{"@type":"Person","name":"Noor Mohammad","url":"https://daily.dev/noormohammad","image":"https://media.daily.dev/image/upload/s--3w4n0n-6--/f_auto/v1754054115/avatars/avatar_eW9hy5zTHEVyYeFaGOP4G?_a=BAMClqZW0"}}],"isPartOf":{"@type":"WebPage","url":"https://daily.dev/sources/ew9hy5zthevyyefagop4g","name":"Noor Mohammad"}} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Noor Mohammad","item":"https://daily.dev/sources/ew9hy5zthevyyefagop4g"},{"@type":"ListItem","position":3,"name":"Chain of Thought Prompting, Explained So You Actually Use It Right (2026)"}]} ```