Artificial Intelligence language models have transformed the way we interact with technology, enabling us to generate human-like text, translate languages, and create content across various domains. However, the effectiveness of these models heavily relies on how we communicate our requests to them. Crafting precise and effective prompts is essential to obtain the desired output from AI models like GPT-4. In this blog post, we’ll explore two frameworks designed to optimize your interactions with AI models: the Elavis Saravia Framework and the CRISPE Framework.

The Elavis Saravia Framework

The Elavis Saravia Framework is a structured approach to formulating prompts that ensure clarity and completeness. It focuses on four key components:

1. Instruction: Clearly define the specific task you want the model to perform. This could be generating text, translating a language, summarizing content, or composing different types of material.

2. Context: Provide the necessary background information the model needs to understand your request fully. For instance, if you want the model to generate text on a specific topic, include relevant details about that topic.

3. Input Data: Supply any specific data that the model should process. In translation tasks, for example, this would be the text you want to translate.

4. Output Indicator: Specify the desired output format or type. If you’re asking for a summary, indicate the length or style you expect.

Example Usage

Suppose you want the model to write a short story about space exploration.

• Instruction: “Write a short story.”

• Context: “The story should be about a team’s first mission to Mars.”

• Input Data: (Not applicable in this case.)

• Output Indicator: “The story should be around 500 words and suitable for young adults.”

By incorporating all these elements, you provide a comprehensive prompt that guides the model effectively.