Artificial intelligence · Grades 9–12
Generative models, tokens and useful prompts · revision notes
An autoregressive language model predicts successive tokens from context. A token may be a word piece, punctuation or another encoded unit.
Key ideas and conditions
Generation
An autoregressive language model predicts successive tokens from context. A token may be a word piece, punctuation or another encoded unit.
Prompting
State the task, relevant context, constraints and desired format. Examples can clarify the task, but they can also bias the response.
Context
The context window is finite and measured in model-specific tokens. Word counts are not a universal token-count conversion.
Checking
Generation can produce plausible unsupported content. Important outputs need verification, and private data should not be included without authorization.
Explain without looking
- A response has 4 claims and independent checking supports 3. Find the supported-claim fraction in this toy audit.
- Which content should not be casually pasted into an external AI service?
Further reading and review references
These are reference links, not copied exercises, institutional endorsement or proof of independent review.
AI-assisted preview; independent educational review required. Level labels describe prerequisites, not accreditation or official curriculum alignment.