Lesson 4

Expanding Content: Encouraging Detailed Responses from LLMs


Today, we explore the strategy of eliciting longer, more detailed responses from Large Language Models (LLMs). In contrast to seeking concise outputs, there are scenarios where depth, detail, and expansiveness enrich the interaction, such as storytelling, in-depth analysis, or comprehensive explanations. Our focus is on mastering the art of crafting prompts that encourage LLMs to "open up" and provide us with the wealth of information they're capable of generating.

The Challenge of Eliciting Detailed Responses

While LLMs are almost never succinct unless specifically asked to be, they are also almost never very thorough unless again they are specifically asked to be.

Here is a simplistic example prompt:

1__ASK__ 2What is the story of Romeo and Juliet?

This would give you a response that's roughly 300-450 words long (depending on the LLM being used). Considering that the actual story is close to 25,000 words, that's not much detail. What if we wanted the LLM to go deeper?

Simple Approach: Requesting Detailed Responses Directly

A direct method to obtain more detailed information is to explicitly ask for it within the prompt's constraints. This strategy relies on clearly communicating your expectations for a detailed or lengthy response.

1__ASK__ 2What is the story of Romeo and Juliet? 3 4__CONSTRAINTS__ 5- Provide a detailed summary, including major themes, character development, and pivotal moments.

This will start to push the response close to 600 words, which is a 50% improvement on what we had so far, not bad.

Complex Approach: Iterative Enhancement

For even greater depth and structure, we can employ an iterative enhancement strategy. This involves first asking for an outline or skeleton of the desired content, then requesting the LLM to elaborate on each part of the outline, and finally asking for further enhancements or details for each section.

Step 1: Request an Outline

1__ASK__ 2Provide an outline for a detailed analysis of "Romeo and Juliet." 3 4__CONSTRAINTS__ 5- Focus on structure, key points, and themes.

Step 2: Request Elaboration on Each Part

1__ASK__ 2Based on the outline provided in the CONTEXT, elaborate on each point with detailed information and analysis. 3 4__CONSTRAINTS__ 5- Ensure each section is comprehensive and well-explained. 6 7__CONTEXT__ 8> OUTPUT_FROM_PREVIOUS_PROMPT

This iterative approach not only encourages an initially structured and detailed response but also builds upon it to achieve an even greater level of depth and elaboration. You can of course add a Step 3 here and repeat the same approach again but you'll start running out of context windows at that point, especially with this specific topic.

Time to Practice

Armed with these strategies, you're now equipped to craft prompts that encourage LLMs to generate longer, more detailed responses. Experimenting with both the simple and complex approaches will help you understand the nuances of prompt design and how to best utilize the capabilities of LLMs for detailed outputs.

Enjoy this lesson? Now it's time to practice with Cosmo!

Practice is how you turn knowledge into actual skills.