Effect of 'Thinking' Prompts on LLM Performance

Chain-of-Thought (COT) Prompting

Multi-Round Interaction to Guide LLM Reasoning

Example of a Prompt for a Direct Mathematical Calculation

Example of a Prompt for Calculating the Average of 1, 3, 5, and 7

Example of a Prompt for Calculating the Mean Square

Improving LLM Reasoning with Step-by-Step Demonstrations

In-Context Learning (ICL)

A user is trying to get a Large Language Model (LLM) to solve a complex word problem that involves multiple calculations. Their initial prompt, 'What is the answer to this problem? [Problem text]', results in a quick but incorrect numerical answer. The user then revises the prompt to: 'First, break down the problem into the necessary steps. Then, solve each step, showing your work. Finally, state the final answer. [Problem text]'. This revised prompt leads to a correct solution. Which principle of interacting with LLMs does this scenario best illustrate?

Evaluating Prompt Strategies for a Logic Puzzle

Prompting for a Reasoning Process to Mitigate Errors in Complex Tasks

Example of a Prompt for Calculating the Average of 2, 4, and 9

Improving LLM Problem-Solving by Demonstrating Reasoning Steps

The Mechanism of Reasoning Prompts

Example of a Prompt with Detailed Reasoning Steps

Chain-of-Thought (COT) Prompting

A user provides a language model with the following prompt: 'A bakery had 80 cookies at the start of the day. They sold 35 cookies in the morning and baked a fresh batch of 50 more. In the afternoon, they sold another 40 cookies. How many cookies are left?' Which of the following model responses best demonstrates an approach where a complete, multi-step reasoning path and the final conclusion are generated together in a single, continuous output?

Chatbot Reasoning Strategy Analysis

Evaluating a Reasoning Strategy for a Customer Support Chatbot

Few-Shot Learning in Prompting

Chain-of-Thought (COT) Prompting

Strategic Information Management in Context Scaling

A developer is using a large language model to classify customer feedback. The model is struggling with ambiguous statements. For the input 'The setup process was a bit of a journey,' the model inconsistently provides different classifications. Which of the following revised inputs best demonstrates the principle of improving performance by extending the model's context with helpful prior information?

Optimizing a Creative Writing Assistant

The Role of Input Context in Model Prediction Quality

Context Scaling via Dynamic External Knowledge

Chain-of-Thought (COT) Prompting

Explicitly Prompting for a Reasoning Process to Prevent Errors

A user wants a language model to solve a multi-step math word problem. The user's prompt includes an example of a different, but structurally similar, word problem along with its final numerical answer. Despite this example, the model fails to solve the new problem correctly. Which statement best analyzes the most probable cause of the model's failure?

Analyzing a Failed Prompt for a Logic Puzzle

Diagnosing LLM Prompting Failures

Example of a Probability-Based Word Problem for LLMs

Example of a Multi-Step Arithmetic Word Problem (Swimming Pool)

Example of a Mathematical Reasoning Word Problem (Jessica's Apps)

Example of a Multi-Step Arithmetic Word Problem (Tom's Marbles)

A large language model was given the following word problem: 'A bakery had 20 muffins. They sold 12 muffins and then baked 3 dozen more. How many muffins does the bakery have now?' The model produced this response: 'First, we start with 20 muffins. They sold 12, so 20 - 12 = 8. Then they baked 3 more, so 8 + 3 = 11. The final answer is 11.' Which statement best analyzes the primary reasoning failure in the model's response?

Chain-of-Thought (COT) Prompting

Example of a Multi-Step Arithmetic Word Problem (Jack's Apples)

Evaluating LLM Arithmetic Inference

A language model is tasked with solving arithmetic word problems. Below are common types of errors it might make when translating language into a sequence of mathematical operations. Match each error type with the scenario that best exemplifies it.

Improving Narrative Coherence in AI-Generated Stories

A developer observes that a language model is generating summaries of long articles that lack detail and miss key points. To address this, they modify the inference process to provide the model with the full, unabridged article text instead of a shorter, pre-processed version. Which statement best analyzes why this modification is likely to improve the quality of the generated summary?

Evaluating Context Expansion for a Chatbot

Few-Shot Learning in Prompting

Chain-of-Thought (COT) Prompting

Retrieval-Augmented Generation (RAG)