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?

One-Shot CoT Prompting

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...

Provided Answer (12) to the Example Arithmetic Reasoning Word Problem

Provided Answer (10) to the Example Arithmetic Reasoning Word Problem

Initial State for the Apple Problem

In-Context Learning (ICL)

A language model is presented with the following problem: "Jack has 7 apples. He ate 2 of them for dinner, but then his mom gave him 5 more apples. The next day, Jack gave 3 apples to his friend John. How many apples does Jack have left in the end?" The model processes the problem and performs the calculation (7 + 5) - 2 - 3, arriving at the correct final answer of 7. Which of the following statements best analyzes the flaw in the model's problem-solving approach?

One-Shot CoT Prompting

Initiating a CoT Response to the Jack's Apples Problem

A language model is tasked with solving the following word problem: 'Jack has 7 apples. He ate 2 of them for dinner, but then his mom gave him 5 more apples. The next day, Jack gave 3 apples to his friend John. How many apples does Jack have left in the end?' Arrange the following computational steps into the correct logical sequence that the model should follow to arrive at the final answer.

Analyzing a Flawed Arithmetic Reasoning Process