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What is a large language model?
An LLM is a model trained to work with language. A useful beginner’s mental model is that it predicts a likely next piece of text, then predicts another, continuing until it has produced a response. This describes text generation at a high level; it is not a complete account of how models are trained or of every model design.
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The model works from context rather than checking each generated statement against a live, authoritative record. Its ability to produce a plausible continuation is different from knowing that the continuation is true. An instructional explanation of attention makes this point directly: attention does not guarantee truth (3Blue1Brown’s explanation of attention).
What are tokens and embeddings?
Tokens are the units the model processes
Before text is handled by a language model, it is divided into tokens. A token may correspond to a word, part of a word, punctuation, or another text unit; it is not necessarily one whole word. The model receives a tokenized representation of the prompt and generates a sequence of tokens in response.
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Embeddings represent tokens numerically
Models use learned numerical representations called embeddings as part of processing tokens. For an introductory lesson, the key distinction is simple: tokenization breaks text into units, while embeddings provide numerical representations that the model can use in its computations.
Why does the Transformer matter?
The Transformer was a major architectural development in language modeling. In their 2017 paper Attention Is All You Need, Ashish Vaswani and seven coauthors proposed an encoder-decoder architecture based solely on attention mechanisms, dispensing with recurrence and convolutions. Their abstract describes it as “a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely” (Vaswani et al., 2017).
That paper is important historical context, not a full description of every current language model. Its reported results—28.4 BLEU on the WMT 2014 English-to-German task and 41.8 BLEU on the WMT 2014 English-to-French task—are results from that paper, not current model benchmarks.
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A short demonstration can make the concepts concrete: inspect how a prompt is tokenized, consider how token representations are used, then ask a pretrained model to continue a short prompt. One proposed introductory workshop outline uses Python and Hugging Face tools for this kind of exercise, but that is an example teaching sequence rather than a requirement for every introduction.
- Choose a short prompt. Use a simple instruction or sentence starter so the generated continuation is easy to inspect.
- Look at its tokenization. Observe that the model’s text units may differ from the word boundaries a reader sees.
- Generate a continuation with a pretrained model. Focus on how the prompt provides context for the generated text; the particular tool or model depends on the lesson setup.
- Check a factual claim in the output. Compare it with dependable evidence rather than treating plausibility or confident wording as proof.
How should you judge an LLM’s answer?
Separate two questions: does the response read naturally, and is its factual content supported? Language generation can produce convincing wording without verifying the claims it contains. For consequential information, check specific claims against dependable sources and follow the evidence rather than the answer’s tone.
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