Topic
Understanding language models
Large language models generate fluent text — not guaranteed knowledge.
Product view — deepdaive workspace
What an LLM does
It predicts likely continuations. That yields dialogue, summaries, and code — and invented facts.
Terms that show up in the news
Model family, parameters, context window, training, inference, benchmarks: once understood, launches can be placed.
Limits
Currency, hallucinations, bias. A new model is a story, not automatic intelligence.
Large language models (LLMs) generate text by predicting likely continuations. That produces fluent answers — not guaranteed knowledge.
Core terms
- Model — the trained system (size, family, version)
- Context window — how much text it can “see” at once
- Training vs inference — learning from data vs producing an answer
- Benchmarks — tests that measure performance, not everyday use
Common misconceptions
- Fluent text is not proof of correctness
- A new model is not automatically “intelligent” in the human sense
- Public demos often show best cases, not the average
Learn more
Prompting shows how tasks get clearer. Learn AI collects the learning paths.
Frequently asked questions
What is the difference between ChatGPT and a language model?
ChatGPT is a product around models (interface, memory, policies). The language model is the text engine underneath.
Why do models contradict each other?
Different training, different data cutoffs, and randomness in generation. That is why we check news and knowledge independently of chat.
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