OpenAI is rolling out the “AI Fundamentals” curriculum with the aim of helping beginners understand artificial intelligence in an easy-to-understand manner. This effort, detailed on the OpenAI News platform, requires no prior technical background.
This course frames AI as a broad category of software that can recognize patterns, learn from data, and generate output, and distinguishes it from a single tool. It focuses on everyday examples such as predictive map routing and chatbot interactions.
Central to the curriculum is an introduction to large-scale language models (LLMs), the engine behind tools such as ChatGPT. These models are described as advanced predictive systems trained on large text datasets, rather than possessing knowledge.
OpenAI details how LLM is developed through two major stages. One is pre-training to learn common language patterns, and the other is post-training to follow instructions and improve performance through safety protocols.
Description of model evolution
Pre-training equips models with a wide range of skills, just as new employees absorb extensive knowledge of the company. After training is the management phase, which guides model clarity, appropriate tone, and policy compliance.
This last stage is critical for safety and focuses on reducing harmful output and handling sensitive topics with care.
Reasoning and immediate response
This course distinguishes between “inferential” and “non-inferential” models. Non-reasoning models, sometimes called “instant,” prioritize speed and fluency for simple tasks like drafting and summarizing.
Reasoning models, or “thinking” models, invest more computational power in step-by-step problem solving. These are suitable for complex analysis, debugging, or decisions with multiple constraints.
For starters, the default ChatGPT experience automatically manages these model types so you can focus on your queries.
The hierarchy is clear. AI is the field, models are the trained systems, LLM is the language, and ChatGPT is an LLM-powered product.
