How deeply does Grokking AI Applications cover chain-of-thought prompting?

Defines step-by-step prompting and pairs it with decomposition and self-validation, without discussing model internals.

Chain-of-thought prompting

As explained in Grokking AI Applications

A prompting technique that asks a language model to express intermediate reasoning steps before presenting a conclusion.

A vague request can produce a vague answer: asking for an energy-drink tagline may return only “Stay energized!” Chain-of-thought prompting makes the task more structured by asking the language model to reason through intermediate steps before giving its conclusion. section 3.2

Use chain-of-thought prompting as part of a practical prompt-enhancement workflow:

Break a complex task into smaller steps, such as listing the sections of a business plan before outlining each section’s goals. section 3.2
Ask the model to explain its reasoning for each step and then provide a final answer. section 3.2
Request self-validation by asking the model to verify its answer or explain why it makes sense. section 3.2
Add a fallback response when the requested information is unavailable, reducing the chance of misleading output. section 3.2

A structured prompt

Instead of asking, “Give me a tagline for an energy drink,” ask the model to generate several options, explain the reasoning behind each choice, select the strongest tagline, and verify that it fits the product. Experiment with the wording and compare the results. section 3.2

How Grokking AI Applications covers it

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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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WordPress Power Toolkit: Harness AI to build next-level websitesChain-of-thought reasoning is a problem-solving technique in which an AI model generates intermediate reasoning steps before producing 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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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.

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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.

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