Prompt Engineering for Generative AI

Chapter 2. Introduction to Large Language Models for Text Generation

In artificial intelligence, a recent focus has been the evolution of large language models. Unlike their less-flexible predecessors, LLMs are capable of handling and learning from a much larger volume of data, resulting in the emergent capability of producing text that closely resembles human language output. These models have generalized across diverse applications, from writing content to automating software development and enabling real-time interactive chatbot experiences.

What Are Text Generation Models?

Text generation models utilize advanced algorithms to understand the meaning in text and produce outputs that are often indistinguishable from human work. If you’ve ever interacted with ChatGPT or marveled at its ability to craft coherent and contextually relevant sentences, you’ve witnessed the power of an LLM in action.

In natural language processing (NLP) and LLMs, the fundamental linguistic unit is a token. Tokens can represent sentences, words, or even subwords such as a set of characters. A useful way to understand the size of text data is by looking at the number of tokens it comprises; for instance, a text of 100 tokens roughly equates to about 75 words. This comparison can be essential for managing the processing limits of LLMs as different models may have varying token capacities.

Tokenization, the process of breaking down text into tokens, is a crucial step in preparing data for NLP tasks. Several ...