Chain of Thought Prompting

Concept

Chain of Thought (CoT) prompting is a technique that elicits intermediate natural language reasoning steps from large language models (LLMs) before producing a final answer. It was introduced by Wei et al. (2022) and is cited as a state-of-the-art prompting method. CoT has been adopted and adapted in code generation (Structured CoT) and is also a known surface for prompt-level backdoor attacks (BadChain).

First seen 7/7/2026

Last seen 7/7/2026

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Chain of Thought Prompting

Definition

Chain of Thought (CoT) prompting is a prompting technique for large language models (LLMs) in which the model is asked to first generate a sequence of intermediate natural language reasoning steps (the "chain of thought") and then produce the final answer or output. The technique was introduced by Wei et al. in the paper Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (NeurIPS 2022) [36].

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Test-Driven Generation of CSPs Using LLMs ← mentions 90% 1e

The paper mentions Chain of Thought as a related reasoning framework.

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CITATIONS

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6 citations — click to expand

[1] Chain-of-thought prompting elicits reasoning in large language models (Wei et al., NeurIPS 2022). Chain-of-thought prompting elicits reasoning in large language models

[2] CoT prompting asks LLMs first to generate intermediate natural language reasoning steps (CoTs) and then output the code; it is described as the state-of-the-art prompting technique for code generation. Structured Chain-of-Thought Prompting for Code Generation

[3] CoT prompting is designed for natural language generation and has low accuracy in code generation, motivating Structured CoT (SCoT) prompting. Structured Chain-of-Thought Prompting for Code Generation

[4] BadChain is the first backdoor attack against LLMs employing CoT prompting, requiring no training data or model parameter access, and inserts a backdoor reasoning step into the model's reasoning chain. BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models

[5] BadChain achieves an average attack success rate of 97.0% across six benchmark tasks on GPT-4, and shuffling-based defenses are overall ineffective. BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models

[6] Chain of Thought prompting is listed alongside LangChain, OpenAI Reasoning, ReAct, and MCP-based frameworks (e.g., MCP-Solver) as a similar 'reasoning framework', but the Test-Driven CSP paper implements its own iterative loop with PyChoco execution and verification feedback. Test-driven Generation of Constraint Satisfaction Problems Using Large Language Models