An Introduction to Applied LLMs
An introduction to applied LLMs, including a discussion of the current state of the field and some of the most important applications.
Large Language Models (LLMs) have revolutionized natural language processing, enabling unprecedented capabilities in text generation, understanding, and task completion. This guide explores the practical aspects of working with LLMs in production environments.
Questions
Frequently Asked Questions
A Large Language Model is an AI system trained on vast amounts of text data that can generate, understand, and manipulate human language. LLMs use transformer architectures and learn by predicting the next token in a sequence during training.
LLMs work by processing input text through layers of neural network computations (transformers) that attend to relationships between all parts of the input simultaneously. They generate responses one token at a time, using probability distributions learned during training on internet-scale text data.
LLMs come in two main categories: base models (trained on raw text prediction) and instruction-tuned models (further trained to follow instructions and be helpful). Most consumer-facing AI assistants use instruction-tuned models built on top of powerful base models.
Frontier LLMs are trained on trillions of tokens of text — roughly equivalent to millions of books. The exact training data composition varies by model, but typically includes web pages, books, code, scientific papers, and other publicly available text.
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