How To Do AI Prompt Templating

Introduction

For a while now, the Learn Till team has been working on a tool that can generate quizzes from text websites and even YouTube videos. They have experimented with different ways to work with prompts, starting from simple text-based prompts to more complex solutions like Lang chain, ultimately settling on Ginger 2. Ginger 2 is a powerful templating engine combined with an open AI API setup for their tool, Learn Till.

In this article, we will explore how they use Python, Ginger 2 templates, and the open AI API to create a simple chat interface for generating prompts. We will break down the steps involved in setting up and using Ginger 2 for AI prompt templating.

To begin, they have developed a function called "chatter" that acts as a wrapper around the open AI API, making it simpler to interact with. They utilize Python's functional programming concepts to create a chat interface that sends queries to the AI model, processes the response, and returns the result.

Next, they utilize Ginger 2 templates to formulate prompts for various scenarios. By incorporating tags and placeholders in the templates, they can easily replace them with actual values like product names, customer details, and even the tone of voice for the prompt.

Finally, they demonstrate how to use the Ginger 2 templates by creating an email template for informing a customer about product unavailability. They show how to process the template using Python and Ginger 2, and then send the generated email template through the chat interface to receive a response.

Overall, this article provides a detailed walkthrough of how to set up and utilize AI prompt templating with Ginger 2, showcasing the integration of Python, templating engines, and AI APIs in a simple and effective manner.

Keywords:

• AI prompt templating
• Ginger 2 templates
• Python functional programming
• Open AI API
• Chat interface
• Prompt generation

FAQ:

1. What is the primary tool discussed in the article? The article focuses on a tool developed by the Learn Till team that can generate quizzes from text websites and YouTube videos by utilizing AI prompt templating.

2. Why did the team choose Ginger 2 for templating? The team experimented with various templating solutions but settled on Ginger 2 due to its capabilities and ease of integration with the open AI API for their tool, Learn Till.

3. How do they interact with the open AI API in Python? They have created a function called 'chatter' that acts as a wrapper around the open AI API, making it simpler to send queries and receive responses for generating prompts.

4. What are the advantages of using Ginger 2 templates for prompt generation? Ginger 2 templates allow for easy formulation of prompts with placeholders that can be replaced with dynamic values, making it efficient to customize prompts for different scenarios or customers.