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title: AI Crash Course: Tokens, Prediction and Temperature
description: LLMs don&#x27;t &#x27;think&#x27; — they are token prediction machines rooted in statistical language modeling dating back to WWII-era cryptography. Tokens are the smallest...
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# AI Crash Course: Tokens, Prediction and Temperature

**[Telerik](https://daily.dev/sources/telerik)** · [@kgn](https://daily.dev/kgn) · 10 min read · 1 upvotes · 0 comments

## Summary

LLMs don't 'think' — they are token prediction machines rooted in statistical language modeling dating back to WWII-era cryptography. Tokens are the smallest units of language a model processes, enabling flexibility and efficiency in understanding and generating text. Temperature controls how often a model picks less statistically likely tokens: low temperature yields predictable, conservative outputs while high temperature produces more creative but error-prone responses. The right temperature depends on the use case. Training data composition also directly drives AI bias, since token probability distributions reflect whatever content the model was trained on.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.telerik.com/blogs/ai-crash-course-tokens-prediction-temperature>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#nlp](https://daily.dev/tags/nlp)

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