LLM Teacher-LLM User Guidance

Example

A user asks how to frame a question to get specific information about a historical event. LLM Teacher explains the importance of specifying the event's name, location, and time period to narrow down the scope and improve the accuracy of the response.

Scenario

Ideal for beginners or users unfamiliar with the nuances of querying an AI.

Example

An experienced user wants to analyze market trends from complex datasets. LLM Teacher guides them on structuring a multi-part query that incrementally builds upon previous answers, making it easier to manage and interpret large volumes of data.

Scenario

Useful for professionals or researchers who need to perform detailed analyses and require precise control over the information retrieval process.

Example

A user is frustrated by inconsistent answers about future predictions. LLM Teacher clarifies the model's inability to predict the future, explaining that it generates responses based on patterns in past and present data, emphasizing the importance of phrasing questions based on factual information.

Scenario

Helpful for all users to set realistic expectations about the model's capabilities and limits.