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

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 building an internal LLM workflow to produc...

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

Problem-Solving Scenarios for Chain-of-Thought Prompting

Self-Consistency Method

Sub-problem Generation in Least-to-Most Prompting

Improving Least-to-Most Prompting with Advanced Techniques

Improving Problem Decomposition in Least-to-Most Prompting

An AI developer needs a large language model to solve a complex, multi-step logic puzzle that requires deducing a final answer from a series of interdependent clues. Initial attempts to solve the puzzle by providing the full puzzle and a few examples of other solved puzzles have consistently failed. Which of the following prompting strategies is the most effective next step, and why?

Analyzing a Problem-Solving Approach

A language model is tasked with solving the following logic puzzle: 'Sarah, David, and Emily are a doctor, a lawyer, and an engineer. The doctor is Emily's sister. David is not the lawyer.' To solve this complex problem, it is broken down into a series of simpler, sequential sub-problems. Arrange the following sub-problems in the correct logical order that builds towards the final solution.

Example of Final Problem Solving in Least-to-Most Prompting

Example of Self-Refinement in Machine Translation

Three-Step Framework for Self-Refinement in LLMs

Ideal Self-Refinement without Additional Training

Fine-Tuning LLMs for Self-Refinement Tasks

Task-Specific Models as an Alternative for Refinement

Self-Refinement as an LLM Alignment Issue

Self-Reflection in LLMs

A developer is using a large language model to generate a Python function for a complex data analysis task. The developer's workflow is as follows:

1. The model generates an initial version of the function.
2. The developer then prompts the same model, providing the initial function and asking it to 'act as a senior code reviewer, identify potential bugs or inefficiencies, and explain how to fix them.'
3. Based on the model's feedback, a final, improved version of the function is produced.

Thi

Applying an Iterative Improvement Framework

Product Design as an Analogy for Self-Refinement

Relationship between Self-Refinement and Self-Reflection in LLMs

Comparing Output Improvement Strategies

Divide-and-Conquer Paradigm

Example of a Classification Task for LLMs: Identifying AI Risks in a Document