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The necessary level of detail in a prompt depends on how well an LLM understands a task, which is often a result of the task's prevalence in pre-training or fine-tuning data. For common and well-understood problems like sentiment analysis, simple instructions are typically adequate. In contrast, for novel classification tasks, prompts must be more descriptive, potentially including detailed task descriptions, specific classification standards, or illustrative examples to guide the model toward correct performance.

Adapting Prompt Detail to an LLM's Task Familiarity

When prompting a Large Language Model for a novel or unfamiliar classification task, providing a detailed description of the problem is crucial for achieving accurate results. This description should include specific details such as the classification standards to be used, ensuring the model fully understands the task's requirements.

Using Descriptive Prompts for Novel Classification Tasks

A developer is using a large language model for two separate text classification jobs. Analyze the following scenarios and explain the likely cause for the performance difference. Then, propose a specific change to the developer's method for the second job to improve its results.

Troubleshooting LLM Performance on a Custom Task

Consider the two prompts below designed for a large language model:

**Prompt A:**
`Classify the sentiment of the following movie review as 'positive', 'negative', or 'neutral'.
Review: [Movie Review Text]`

**Prompt B:**
`You are an AI assistant for a new tech startup. Your task is to categorize customer feedback into one of three custom labels: 'Feature Request', 'Bug Report', or 'Usability Issue'.
- 'Feature Request' suggests a new capability.
- 'Bug Report' describes something that is broken or not working as intended.
- 'Usability Issue' describes a part of the product that is confusing or difficult to use.
Feedback: [Customer Feedback Text]`

Explain why Prompt B must include significantly more detail than Prompt A to achieve a reliable and accurate classification from the model.

Analyzing Prompt Complexity

A developer needs to use a large language model for two distinct purposes. The first task is to summarize long news articles into a single paragraph. The second task is to categorize employee performance notes into a new, company-specific framework with three tiers ('Exceeds Expectations', 'Meets Expectations', 'Needs Development'), based on a unique set of internal performance indicators. Which of the following prompting strategies would be most effective?

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