AI fundamentals

AI fundamentals Understand the basics of AI, including what it is, how it works, and how it’s used. 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: - Your map app reroutes you around traffic - Your bank flags a purchase as “unusual” - A customer support chatbot answers common questions 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. OpenAI and other frontier research labs build these models as a core part of their offerings, then make them available through user-facing products (like ChatGPT or Codex) and through APIs, which let developers use those models to build their own AI tools and integrate AI into existing software. New models become available from these research labs when they have been trained and passed internal evaluation and safety testing. When you hear that an AI model was “trained,” it usually refers to two stages—think of it like someone learning and getting better at their job. 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. Think of it like a new employee who spends weeks reading everything they can—manuals, examples of great work, past projects, FAQs—until they understand the “shape” of the job. Now the “employee” starts doing the work, and a “manager” coaches them: be clearer, ask good follow-ups, match the right tone, and follow company policies. That’s post-training. This stage helps the model follow instructions more reliably, communicate in a useful style, and handle tricky situations better. Post-training is also where safety checks get emphasized—training that is designed to reduce harmful outputs, avoid unwanted requests, and respond more carefully when the topic is sensitive or uncertain. As models are updated and trained, you might notice shifts in tone or responses.…