AI fundamentals
OpenAI explains that artificial intelligence is a broad category of software that recognizes patterns, learns from data, and produces useful outputs. Within AI are models, such as large language models (LLMs), that learn from vast text to generate and transform language. The article outlines how LLMs are trained in pre‑training and post‑training stages, how they are offered through products like ChatGPT and APIs, and how different model variants balance speed, depth, and safety.
The Core Development
Welcome! If you’re new to AI, you don’t need a technical background to get started. What helps most is a simple map of the landscape—so you can understand what AI systems can do, how they’re packaged, and how to choose the right tool for your needs.
Artificial intelligence (AI) is a broad category of software that can recognize patterns, learn from data, and produce useful outputs.
You’ve probably seen AI show up in everyday moments, like when:
Deployment & Availability
As models are updated and trained, you might notice shifts in tone or responses. If you want consistent results, be explicit about your goal, audience, format, and constraints—and expect the model to be more careful when safety or uncertainty is involved.
Strategic Perspective & Ecosystem Impact
AI is a category—not one single tool. Within that category are models : trained systems that learn from data and then apply what they’ve learned to new situations. Some models specialize in speech, vision, or forecasting.
You’re likely starting your AI journey by using conversational AI tools, like ChatGPT. The models behind ChatGPT specialize in language—these are called large language models .
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 doesn’t “know” things the way a person does. Instead, it predicts the most likely next piece of language based on context. Over time, advances in computing power, training methods, and access to large datasets made it possible to build larger and more capable large language models.
Why it matters
Understanding the training stages and model variants helps developers and users select appropriate AI tools, improving efficiency and safety in AI deployments.