Must one token equal one English word?
- Yes, for every model
- No
- Yes, in every language
- Only if a prompt is short
Answer and explanation
No
Tokenization depends on the vocabulary and encoding.
Subjects
Generative ai
Choose questions individually, practise online, or create a worksheet with matching answers. Counts include generated variants, not only authored originals.
Make a worksheet351 question instances
Sample exercises
Which prompt is better specified for a study task?
- Summarize the supplied passage in five factual sentences and flag missing evidence
- Do everything perfectly
- Guess facts not in the passage
- Hide uncertainty and invent citations
Answer and explanation
Summarize the supplied passage in five factual sentences and flag missing evidence
A clear task and evidence boundary are testable.
A toy context budget is 1000 tokens with 700 reserved for input. How many tokens remain for output under that simple shared-budget model?
Answer and explanation
300
1000−700=300; actual system budgets must follow their documented rules.
Does adding a request to be accurate guarantee factual accuracy?
- Yes, always
- Only if repeated ten times
- No
- Only in capital letters
Answer and explanation
No
Instructions can help but do not replace verification.
Why should example answers be chosen carefully in a prompt?
Answer and explanation
They teach format and task patterns but may introduce bias, unwanted assumptions or misleading examples.
Credit both benefit and risk.
Rewrite a vague request to make a verifiable summarization task.
Answer and explanation
Specify the source text, audience, length, format, and a rule not to add unsupported claims; ask to identify uncertainty or missing information.
Credit a concrete task specification rather than one mandatory wording.
Study notes
Generative models, tokens and useful prompts
An autoregressive language model predicts successive tokens from context. A token may be a word piece, punctuation or another encoded unit.
Grades 9–12Generative 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.
Grades 9–12Retrieval-augmented generation and evidence grounding
Retrieval-augmented generation first retrieves relevant source material, then conditions generation on selected context. It does not automatically make every answer correct.
UniversityRetrieval-augmented generation and evidence grounding · revision notes
Retrieval-augmented generation first retrieves relevant source material, then conditions generation on selected context. It does not automatically make every answer correct.
UniversityEvaluating generated answers and retrieval systems
Use representative tasks with clear expected behaviour and deliberately include failure cases. Keep an evaluation set separate from prompt tuning.
UniversityEvaluating generated answers and retrieval systems · revision notes
Use representative tasks with clear expected behaviour and deliberately include failure cases. Keep an evaluation set separate from prompt tuning.
UniversityRetrieval recall on a labeled evidence set
The reference relevance labels and query set define the evaluation; recall does not measure answer correctness.
UniversityRetrieval recall on a labeled evidence set · formula reference
The reference relevance labels and query set define the evaluation; recall does not measure answer correctness.
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