A Short Intro to Prompt Engineering And Generative AI

To understand what prompt engineering is, we first need to understand the concept of generative AI. Generative AI is a technology that uses artificial intelligence to create data. This data can include text, images, audio, video, and even code. Prompt engineering involves designing inputs to get the best results from generative AI models, especially language models.

Generative AI is evolving rapidly, with new models and technologies emerging regularly. Some notable examples where prompt engineering plays a crucial role include:

Language Models:

• GPT-1, GPT-2, GPT-3, ChatGPT: These models from OpenAI have progressively improved in generating human-like text based on the prompts given.
• Jurassic Model: Another powerful language model known for its large-scale capabilities.
• GPT-J: An open-source large language model that offers powerful text generation.

Text-to-Image Models:

• DALL-E, MidJourney, Stable Diffusion: These models can create images from text descriptions, demonstrating the versatility of generative AI beyond just text generation.

Other Models:

• BERT: Developed by Google, this model excels in understanding the context of words in a sentence, making it highly effective for various natural language processing tasks.
• Bard: Uses LamDA to generate sophisticated responses, adding another layer of complexity to generative AI.

In the realm of generative AI, the term "token" frequently comes up. A token is a small unit of text that a large language model can easily understand. Tokenization is the process of splitting text into these small units.

Word vs. Token:
A single word can consist of multiple tokens. Some words are represented by one token, while others require more. Different models have unique ways of tokenizing input, and this process can significantly impact the model's performance and output.

Generative AI and large language models rely heavily on this tokenization process to function effectively. Different models use different tokenization methods, which can lead to variations in their outputs. Understanding how tokenization works and how different models handle it is key to mastering prompt engineering.

This covers basic part in later post we will dive into it with some sites and example until that Happy Coding Happy Learning 🥳🎉