OpenAI AI fundamentals overview

TL;DR

OpenAI published an educational guide titled AI fundamentals that defines AI, describes large language models, outlines pre‑training and post‑training, distinguishes reasoning from non‑reasoning models, and shows how ChatGPT fits into the hierarchy.

What is AI?

Artificial intelligence (AI) is a broad category of software that can recognize patterns, learn from data, and produce useful outputs.

"Artificial intelligence (AI) is a broad category of software that can recognize patterns, learn from data, and produce useful outputs." AI appears in everyday tools such as map apps that reroute around traffic, banks that flag unusual purchases, and customer‑support chatbots that answer common questions. AI is a category, not a single tool; within it are models that specialize in tasks like speech, vision, or forecasting. Beginners often start with conversational AI tools like ChatGPT, which are powered by large language models.

How large language models work

A large language model (LLM) is a model designed to work with language. It learns patterns from large amounts of text from many sources so it can generate and transform text in helpful ways.

"A large language model (LLM) is a model designed to work with language. It learns patterns from large amounts of text from many sources so it can generate and transform text in helpful ways." An LLM does not "know" things like a person; instead, it predicts the most likely next piece of language based on context. Advances in computing power, training methods, and access to large datasets have enabled the construction of larger, more capable LLMs. Frontier labs such as OpenAI build these models and make them available through products like ChatGPT or Codex and through APIs for developers.

How models evolve over time

New models become available after they have been trained and passed internal evaluation and safety testing. Training consists of two stages: pre‑training and post‑training.

"The first stage is pre‑training, when the model learns general patterns from a huge amount of text, which gives it broad skills like summarizing, drafting, translating, and explaining." Pre‑training is analogous to a new employee reading manuals, examples, and past projects to understand the shape of the job. After pre‑training, the model enters post‑training, where it is coached to be clearer, ask good follow‑ups, match the right tone, and follow company policies. This stage also emphasizes safety checks designed to reduce harmful outputs, avoid unwanted requests, and respond carefully on sensitive or uncertain topics. As models are updated, users may notice shifts in tone or responses; being explicit about goal, audience, format, and constraints helps obtain consistent results, especially when safety or uncertainty is involved.

Reasoning and non‑reasoning models

Models are tuned for different tradeoffs such as speed, depth, and ability to follow multi‑step instructions.

"Non‑reasoning models (sometimes labeled as ‘Instant’) are optimized for fast, fluent output. They’re a good default when the task is straightforward and you mainly want momentum." "Reasoning models (sometimes labeled as ‘Thinking’) are trained to do better at deliberate, step‑by‑step problem solving—things like planning, complex analysis, tricky debugging, or decisions with constraints and edge cases." Non‑reasoning models prioritize speed and fluency for everyday tasks like drafting, summarizing, rewriting, or brainstorming. Reasoning models allocate more compute to think through a problem before answering, which can improve reliability on harder, multi‑step work, though they may take longer. For beginners, the default ChatGPT experience auto‑switches between modes so users can focus on the question rather than settings. Over time, users can experiment with optional controls, choosing Auto most of the time and switching to Thinking for complex or high‑stakes tasks.

Summary

The guide presents a simple hierarchy:

• AI = the overall field
• Models = trained systems that perform particular tasks
• Large language models (LLMs) = models focused on understanding and generating language, trained over time by AI research labs
• ChatGPT = a product that helps you use an LLM effectively Understanding this hierarchy prepares users to get great results from tools like ChatGPT by learning how to communicate with them effectively.