Glossary
A plain-English glossary of the methods and ideas behind modern AI. Search, sort, or jump to a letter.
10 terms
| Definition | ||
|---|---|---|
| Temperature | A sampling setting that controls randomness — low values make output focused and deterministic, high values make it more diverse. | May 22, 2026 |
| Test-Time Compute Scaling | Improving accuracy by spending more computation at inference — longer reasoning or multiple samples — rather than only at training. | May 21, 2026 |
| Token | The atomic unit of text a model reads and generates — typically a word, sub-word, or character chunk. | May 22, 2026 |
| Tokenization | Splitting text into the discrete units a model actually reads. | May 21, 2026 |
| Tool Use | Letting models call external tools, code, and APIs. | May 21, 2026 |
| Top-p (Nucleus) Sampling | Sampling the next token from the smallest set of candidates whose probabilities sum to p. | May 22, 2026 |
| Transfer Learning | Reusing a model trained on one task to bootstrap another. | May 21, 2026 |
| Transformer | The neural-network architecture behind virtually all modern language models. | May 21, 2026 |
| Tree of Thoughts | Reasoning by exploring and evaluating multiple solution paths. | May 21, 2026 |
| Turing Test | Alan Turing’s thought experiment for machine intelligence. | May 21, 2026 |