Activating LLM Reasoning with Prompts

Explicitly Prompting for a Reasoning Process to Prevent Errors

Complex Problems

Iterative Methods in LLM Prompting

Prompt Ensembling

Automatic Generation of Demonstrations and Prompts with LLMs

Prompt Augmentation

Leveraging LLM Output Variance

Few-Shot Learning in Prompting

Chain-of-Thought (CoT) Reasoning

Zero-Shot Learning with LLMs

Improving LLM Performance on a Reasoning Task

A developer is prompting a Large Language Model to solve a complex multi-step word problem. Initial attempts, which only asked for the final answer, resulted in frequent errors. The developer then modified the prompt to include a similar word problem, followed by a detailed, step-by-step explanation of how to arrive at the correct solution, and finally the solution itself. Which prompting technique is most central to this improved prompt's design, and what is its primary benefit in this context?

Match each prompting technique with the description that best defines its core approach.

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