Google

The principles of prompt optimization extend beyond instructions to other components, such as demonstrations. A significant area of research focuses on automatically learning to select or generate the most effective demonstrations, particularly for techniques like Chain-of-Thought (CoT) prompting.

Optimizing Prompt Demonstrations

A developer is creating a prompt to classify customer reviews into 'Positive', 'Negative', or 'Neutral' categories. Analyze the set of demonstrations provided in the prompt below. What is the primary weakness of this set of demonstrations, and why could it lead to poor performance when the model is used on a diverse set of new, unseen reviews?

Evaluating Prompt Demonstration Quality

A developer is creating a prompt to solve multi-step math word problems. The prompt includes several examples of problems and their final answers. However, the model frequently makes logical errors on new, unseen problems. Based on principles for optimizing in-context examples, what is the most likely flaw in the prompt's design?

A developer is using a few-shot prompt to teach a language model to solve simple logic puzzles. The model's performance is inconsistent. Below is one of the demonstrations included in the prompt:

**Input:** 'If all Wibs are Wobs, and all Wobs are Wubs, are all Wibs also Wubs?'
**Output:** 'Yes.'

Analyze this demonstration. Identify a key weakness in its structure and explain how you would modify it to more effectively guide the model's reasoning process.

Learn Before

Related