> Documentation Index > Fetch the complete documentation index at: https://docs.replit.com/llms.txt > Use this file to discover all available pages before exploring further. Introduction to AI > Learn the practical AI concepts behind Replit Agent — large language models, tokens, variability — and how AI agents combine models with tools to build with you. AI tools help you give instructions in natural language and get useful output back. On Replit, that output can become plans, code, designs, explanations, debugging steps, and changes to your project. You do not need to understand AI deeply before building. A few concepts help you give better instructions and evaluate the results with confidence. Large language models Most AI builders are powered by large language models, often called LLMs. An LLM is a model trained to recognize patterns in text and generate text that follows those patterns. Text can mean prose, code, HTML, CSS, SQL, JSON, terminal commands, test output, error messages, or configuration. That is why the same kind of model can help write copy, explain a bug, create code, or summarize a plan. Text generation An LLM does not retrieve a fixed answer from a database. It generates a response piece by piece based on the input you provide and the context it can see. When you write a prompt, the model uses your words, the conversation, and any available project context to predict a useful next response. That response might be an explanation, a plan, or text that another system can use to take action. This means the quality of your input matters. Clear goals, examples, constraints, and context make good results more likely. Tokens AI models process text in small units called tokens. A token can be a word, part of a word, punctuation, whitespace, or a piece of code. Tokens matter because they affect how much information an AI model can consider at once. Long conversations, large files, logs, screenshots, and instructions all c