Chain-of-thought prompting

As explained in WordPress Power Toolkit: Harness AI to build next-level websites

Chain-of-thought reasoning is a problem-solving technique in which an AI model generates intermediate reasoning steps before producing a final answer.

Chain-of-thought prompting asks an AI to work through a task in smaller, ordered steps rather than jump straight to an answer. For a complex website decision, this can slow the exchange down, preserve context, and make it easier to inspect and improve the result. section 1.5.11 section 12.6.1

The useful part is not merely getting a longer reply. Break a goal into stages, ask for headings or a conclusion, and pause for your input between stages. Your answers give the AI details—such as visitor goals or priorities—that can sharpen the next step. section 12.6.1

Use this approach when a one-shot prompt is too vague:

• State the overall goal, such as improving a website design or reviewing its analytics. section 12.6.1
• Ask the AI to proceed step by step and organize the response with headings. section 12.6.1
• Require a pause after each step so you can supply goals, corrections, or missing context. section 12.6.1
• Verify the output and continue the conversation until the final response is focused enough to use. section 12.6.1
• For extra checking or polish, paste one AI’s response into a second AI with a specific revision request. section 12.6.1

A staged website-design prompt

Try: “Taking this step by step, tell me what professional reviewers think are best practices when designing a website. Start with an introduction, then after each step ask me for my input. End with a conclusion. Provide headings for each step.” The pauses turn the response into a guided conversation instead of a generic checklist. section 12.6.1

Some models can also reason through multistep tasks internally. GPT o1 is described as breaking a problem into parts, analyzing them, and synthesizing a final answer; those internal steps may not always be shown. In agent settings, this capability can help an AI choose subgoals and actions for a larger goal with less detailed prompting. section 1.5.11 section 12.5.1

Figure 12.2 Chat with multiple AIs at the same time with the ChatHub Chrome extension.

A multi-AI chat interface supports the second-opinion workflow: compare responses to the same request, then use another model for corroboration, deeper insight, or clearer wording. section 12.6.1

How WordPress Power Toolkit: Harness AI to build next-level websites covers it

How deep it goes

Explains staged prompting through a full website-design prompt and multi-AI revision workflow, while noting hidden model reasoning without detailing internals.

Examples

Uses a stepwise website-design discussion and analytics-data rewriting between Meta and Perplexity.

What you can do after

Apply it by structuring a goal into interactive steps and corroborating or polishing results with another AI.

How it connects

Connects chain-of-thought reasoning with AI agents, logical decomposition, website design, website analytics, and multimodal AI.

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Also explained in

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  Introduction to Generative AI, Second EditionChain-of-thought prompting is a technique that elicits intermediate reasoning steps before a model produces its final answer.
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  Coding with AIChain-of-thought prompting is a prompting technique that guides an AI model through a problem using a sequence of intermediate reasoning steps.
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  AI Agents in Action, Second Edition
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  The Art of AI Product DevelopmentChain-of-thought is a prompting technique that elicits an intermediate sequence of reasoning steps before a model produces a final answer.
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  LLMs in ProductionChain-of-thought prompting encourages a language model to produce or use intermediate reasoning steps before giving a final answer.
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  Build AI-Enhanced Web AppsA prompting technique that guides a language model through a single sequential reasoning path.
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  Prompt Engineering in PracticeA reasoning or prompting technique in which a model generates intermediate steps toward an answer.
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  Grokking AI ApplicationsA prompting technique that asks a language model to express intermediate reasoning steps before presenting a conclusion.
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  AI Agents and Applications
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  Generative AI in ActionChain of thought is a prompting technique that decomposes a problem into intermediate reasoning steps before producing an answer.
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  Sutskever's ListChain-of-thought prompting is a prompting technique that elicits explicit intermediate reasoning steps before a model produces its final answer.
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  Building Reliable AI SystemsChain-of-thought prompting is a prompting method that elicits explicit intermediate reasoning steps from a model.
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  Time Series Forecasting Using Foundation ModelsChain-of-thought prompting is a prompting technique that elicits intermediate reasoning steps before a model produces an answer.
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  Transformers in ActionChain-of-thought prompting guides a language model by providing or eliciting a sequence of intermediate reasoning steps before the final answer.
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  How Large Language Models WorkA prompting technique that encourages a language model to produce an intermediate sequence of reasoning steps before an answer.
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  Reinforcement Learning from Human FeedbackA reasoning trace is a sequence of generated textual intermediate steps used while solving a problem before reaching an answer.
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  Deep Learning with Python, Third EditionA search-based inference approach that explores reasoning in natural language by generating intermediate thought steps before an answer.

Compare how 18 books explain chain-of-thought prompting →

Related concepts

• prompt engineeringkind of · Prompt engineering is the deliberate design and refinement of instructions, context, examples, or other inputs to guide an AI model’s behavior or outputs.
• language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• multi-step reasoninguses · Multi-step reasoning derives a conclusion, solution, or decision through multiple connected intermediate inferential or problem-solving steps.
• Tree-of-thoughtcontrasts with · Tree-of-thought is a reasoning technique that explores and evaluates multiple branching paths of intermediate reasoning to develop a solution.
• self-consistencyused by · Self-consistency is a technique that generates multiple reasoning paths or responses for the same input and selects or combines results based on their agreement.

This explainer was generated by Manning's knowledge graph from the text of WordPress Power Toolkit: Harness AI to build next-level websites. The section links point to the parts of the book it draws on.