Prompt Generation Using Retrivel

# Simplifying Prompt Generation for LLMs with Retrieval Methods

Working with Large Language Models (LLMs) like ChatGPT often involves feeding
them prompts: short texts that give the model direction on how to respond.
Manually crafting these prompts can be tedious. Wouldn't it be convenient if we
could automate this process, ensuring each prompt is contextually relevant?
Let's explore a method that does just that, using the Yival framework and the
FAISS vector database.

## **Why Automate Prompt Generation?**

Think of prompts as questions or instructions you give to ChatGPT. The more
precise the instruction, the better the model's answer. But crafting a new
instruction for every unique scenario is time-consuming. If we could automate
this, not only would it save time, but it'd also ensure the AI's responses are
consistently relevant.

## **Storing Prompts: FAISS to the Rescue**

We source a wide variety of prompts from [awesome-chatgpt-prompts](https://github.com/f/awesome-chatgpt-prompts).
To quickly find the best prompt for any situation:

- **We use FAISS**: This tool helps store prompts in a way that makes them quick
  to search and retrieve.
- **We turn prompts into 'vectors'**: By transforming prompts into a mathematical
  format (vectors), we can easily find the most fitting one for a given situation.

## **How Yival Helps in Retrieval**

Yival is like a toolbox that simplifies AI-related experimentation.
When integrated with FAISS, it streamlines the process of fetching the right prompt.

### **The Main Steps**

1. **Find Matching Prompts**: Based on a given situation, Yival searches the FAISS
    database to find similar prompts.
2. **Refine with GPT**: Sometimes, the initially found prompts might not be perfect.
    So, we use GPT to rerank them, ensuring we pick the most suitable one.

## **Putting It All Together**

With our setup, generating a prompt becomes straightforward. For ChatGPT,
here's a simple example of how we might use a generated prompt: