Artificial intelligence algorithms had been ticking away for years before ChatGPT arrived. These systems were a big departure from traditional programming, which follows a rigid if-this-then-that approach. AI, in contrast, is trained to spot patterns in complex real-world data. AI has been busy for more than a decade screening out spam, identifying our friends in photos, recommending videos and translating our Alexa voice commands into computerese.

A Google technology called transformers helped propel AI to a new level, leading to a type of AI called a large language model, or LLM. These AIs are trained on enormous quantities of text, including material like books, blog posts, forum comments and news articles. The training process internalizes the relationships between words, letting chatbots process input text and then generate what it believes to be appropriate output text. 

A second phase of building an LLM is called reinforcement learning through human feedback, or RLHF. That's when people review the chatbot's responses and steer it toward good answers or away from bad ones. That significantly alters the tool's behavior and is one important mechanism for trying to stop abuse.

OpenAI's LLM is called GPT, which stands for "generative pretrained transformer." Training a new model is expensive and time consuming, typically taking weeks and requiring a data center packed with thousands of expensive AI acceleration processors. OpenAI's latest LLM is called GPT-4 Turbo. Other LLMs include Google's Gemini (formerly called Bard), Anthropic's Claude and Meta's Llama.

ChatGPT is an interface that lets you easily prompt GPT for responses. When it arrived as a free tool in November 2022, its use exploded far beyond what OpenAI expected.

When OpenAI launched ChatGPT, the company didn't even see it as a product. It was supposed to be a mere "research preview," a test that could draw some feedback from a broader audience, said ChatGPT product leader Nick Turley. Instead, it went viral, and OpenAI scrambled to just keep the service up and running under the demand.

"It was surreal," Turley said. "There was something about that release that just struck a nerve with folks in a way that we certainly did not expect. I remember distinctly coming back the day after we launched and looking at dashboards and thinking, something's broken, this couldn't be real, because we really didn't make a very big deal out of this launch."