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  • Chain-of-Thought (COT) Prompting

Zero-Shot Chain-of-Thought (COT) Prompting

Zero-shot Chain-of-Thought (COT) is a prompting technique that elicits step-by-step reasoning from a large language model without providing any preliminary examples or intermediate reasoning steps. Instead, it relies on adding a simple instructional trigger to the prompt—such as appending the phrase "Let's think step by step."—which provokes the model to independently generate its own reasoning process to reach the final answer.

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2 days ago

Contributors are:

Gemini AI
Gemini AI
🏆 19

Who are from:

Google
Google
🏆 19

References

Tags

Ch.2 Generative Models - Foundations of Large Language Models

Foundations of Large Language Models

Foundations of Large Language Models Course

Computing Sciences

Ch.3 Prompting - Foundations of Large Language Models

Related

  • Application of COT Prompting on GSM8K Benchmark

  • Structuring Logical Reasoning Steps for Demonstrations

  • Zero-Shot Chain-of-Thought (COT) Prompting

  • Application of CoT to Algebraic Calculation Problems

  • Benefits of Chain-of-Thought (CoT) Prompting

  • Incomplete Answers from Zero-Shot CoT Prompts

  • Chain-of-Thought as a Search Process

  • Supervising Intermediate Reasoning Steps for LLM Alignment

  • Limitations of Simple Chain-of-Thought Prompting

  • Creating a CoT Prompt by Incorporating Reasoning Steps

  • Alternative Trigger Phrases for Zero-Shot CoT Prompting

  • Incomplete Answers as a Potential Issue in Zero-Shot CoT Prompting

  • A developer is trying to improve a language model's ability to solve multi-step word problems. They compare two prompting strategies.

    Strategy 1: Provide the model with a new word problem and ask for the final answer directly.

    Strategy 2: Provide the model with a new word problem, but first show it an example of a similar problem where the solution is explicitly broken down into logical, sequential steps before reaching the final conclusion.

    Why is Strategy 2 generally more effective for improving the model's reasoning on complex tasks?

  • Improving a Prompt for a Multi-Step Problem

  • Few-Shot Chain-of-Thought (CoT) Prompting

  • Practical Limitations of Chain-of-Thought Prompting

  • The primary benefit of a prompting technique that demonstrates a step-by-step reasoning process is that it permanently modifies the language model's internal weights, making it inherently better at solving similar problems in the future, even without the detailed prompt.

  • Designing a Prompting Workflow for a High-Stakes, Multi-Step Task

  • Choosing and Justifying a Prompting Strategy Under Context and Quality Constraints

  • Diagnosing and Redesigning a Prompting Approach for a Decomposed Workflow

  • Stabilizing an LLM Workflow for Multi-Step Policy Compliance Decisions

  • Debugging a Multi-Step LLM Workflow for Contract Clause Risk Triage

  • Designing a Robust Prompting Workflow for Multi-Step Root-Cause Analysis with Limited Examples

  • You’re building an internal LLM assistant to help ...

  • Your team is rolling out an internal LLM assistant...

  • You’re leading an internal enablement team buildin...

  • You’re building an internal LLM workflow to produc...

  • Example of One-Shot Chain-of-Thought (COT) Prompting

Learn After

  • Example of a Zero-Shot COT Prompt

  • Comparison of Few-Shot and Zero-Shot CoT Prompting

  • Alternative Phrases for Triggering Chain-of-Thought Reasoning

  • A user wants a large language model to solve a multi-step word problem. The model's initial attempts provide only a final, incorrect answer. The user's goal is to modify the prompt to encourage the model to generate a detailed, step-by-step thought process first, which should lead to a more accurate final answer. Crucially, the user does not want to include a complete, solved example of another problem in the prompt. Which of the following prompt modifications best achieves this specific goal?

  • To successfully prompt a language model to generate a step-by-step thought process for a new problem, one must always include a complete, solved example of a similar problem within the prompt.

  • Structure of a Zero-Shot CoT Prompt for an Arithmetic Task

  • Identifying a Zero-Shot Reasoning Prompt

  • Your team is rolling out an internal LLM assistant...

  • You’re building an internal LLM workflow to produc...

  • You’re building an internal LLM assistant to help ...

  • You’re leading an internal enablement team buildin...

  • Choosing and Justifying a Prompting Strategy Under Context and Quality Constraints

  • Designing a Prompting Workflow for a High-Stakes, Multi-Step Task

  • Diagnosing and Redesigning a Prompting Approach for a Decomposed Workflow

  • Stabilizing an LLM Workflow for Multi-Step Policy Compliance Decisions

  • Debugging a Multi-Step LLM Workflow for Contract Clause Risk Triage

  • Designing a Robust Prompting Workflow for Multi-Step Root-Cause Analysis with Limited Examples

  • Zero-Shot CoT Example with Jack's Apples