TL;DR
Large language models answer questions not by a single magic trick but by combining three mechanisms — parameters, reasoning, and web access.
Have you ever wondered why AI can answer your questions? It is not an encyclopedia, so how does it know so much?
Actually, the secret lies in three mechanisms. Once you understand them, you have a basic grasp of AI.
Parameter mechanism: compressing knowledge into numbers
Every large model does one thing: it models human knowledge. How? It splits text into tiny pieces (tokens) and calculates the mathematical relationships between them.
These relationships are stored in massive numbers of parameters. GLM 5.3, for instance, has 744 billion parameters. Training means finding the best values so that the token relationships are as accurate as possible.
When answering, the model uses those parameters to compute the most likely next word. So a large model is essentially a compress-and-generate machine: knowledge is compressed into parameters, and answers are generated from them probabilistically.
Reasoning and web access: filling the knowledge gaps
More parameters help, but you cannot know everything — and you do not need to. Much knowledge can be reasoned out. For example, knowing birth and death rates lets you calculate net population growth without memorizing it.
Other knowledge, like today's stock market closing index, is in neither parameters nor reasoning. That is where web access comes in — the AI automatically searches the internet for answers.
Each mechanism has its role: parameters supply the base, reasoning adds logic, and web access handles real-time information. Together, they make AI seem omniscient.
Curated from high-quality sources, with concise summaries and key takeaways.
