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How does generative AI work?
Generative AI is a class of systems that produces new content by learning patterns or characteristics from input data. That content can be text, images, audio or video; NIST’s glossary definition covers these different forms.
A useful way to understand a text model is to separate two phases: training, when a model’s internal parameters are adjusted using data, and generation (also called inference), when a trained model uses a prompt and its learned patterns to produce an output. These phases are related, but they are not the same activity.
| Phase | What happens |
|---|---|
| Training | The model processes examples and adjusts parameters to improve its predictions. The data sources and methods depend on the model provider. |
| Generation or inference | The trained model uses its parameters and the current input to produce an output. A text model commonly generates a sequence of tokens. |
How does an AI learn?
During training, a model is exposed to examples and learns statistical relationships in them. For language models, a common training approach uses prediction: the model learns to predict text elements from context, and its parameters are adjusted to make its predictions more useful. This is not the same as a person reading every page and remembering it as a collection of facts.
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Training data and procedures are not identical across providers. OpenAI, for example, describes its own models as developed using publicly available information, third-party information, and information supplied or generated by users, human trainers and researchers. That is an account of OpenAI’s approach, not a universal recipe for all generative AI.
What is a token in AI?
A token is a unit of text a model processes. Depending on the tokenizer, a token may represent a whole word, part of a word, punctuation or another text fragment. For example, a long or uncommon word may be split into multiple tokens. Tokens are therefore not simply a count of words; OpenAI’s API concepts guide explains tokenization and gives examples.
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A neural network’s parameters are adjustable values that help determine how it processes input and predicts what comes next. Training changes those values. The model then uses the resulting parameters when responding to new input.
What do transformers do?
Many language models use transformer-based architectures. NIST defines a generative pre-trained transformer as a transformer-based model pre-trained through self-supervised learning on large, unlabelled text datasets in its GPT glossary entry.
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One important transformer mechanism is self-attention: it helps the model weigh how relevant different tokens are to one another in context. A rough analogy is choosing a likely continuation while looking back at the surrounding sentence rather than considering a word in isolation. The actual process is mathematical; the analogy does not mean the model understands text as a person does. Google’s LLM guide explains tokens, prediction, parameters and attention.
How does AI generate text?
When a user supplies a prompt, a text-generation model processes it as tokens and uses its learned parameters and the context so far to estimate a likely next token. It generates a token, adds that to the context, and continues until it reaches a stopping condition or the system’s output limit. Because several continuations may be plausible, the same prompt can produce different wording or answers.
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Google’s article quotes senior research director Douglas Eck describing the basic idea: “Language models basically predict what word comes next in a sequence of words.” It is a helpful shorthand for language models, not a full description of every generative AI system.
Image, audio and video generators also learn patterns, but they work with representations suited to those media. It would be misleading to describe every such system as simply predicting the next word.
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What happens after initial training?
Pre-training is not always the final step before a model is used. Providers may post-train models, evaluate them, or use instruction tuning to improve how they respond to requests. OpenAI describes training, post-training, evaluation and ongoing improvement for its models; Google’s developer guide describes instruction tuning as one way to improve instruction following. The specific stages and methods vary by model.
Some deployed systems can also retrieve information or use tools at runtime. Retrieval-augmented generation, for example, can provide a model with retrieved material to use when producing an answer. This is distinct from information encoded in the model’s learned parameters, and it does not mean every model browses the web for every response. Google Cloud’s generative AI glossary covers retrieval-augmented generation and other terms.
Why can generative AI sound sure and still be wrong?
A text model is designed to generate plausible continuations, not to guarantee that each statement has been checked against reality. A response can be coherent and fluent while containing an error, an invented detail or bias. Google lists hallucinations and bias among the challenges associated with large language models.
- Check factual claims against reliable sources, especially for medical, legal, financial, safety-related or other consequential decisions.
- Look for primary sources when a response gives a statistic, quotation, date or technical claim.
- Do not assume a system searched current information unless its product explicitly indicates that it used search or another retrieval tool.
Generative AI can be useful for drafting, explaining or exploring ideas, but its output should be treated as generated content rather than automatic verification.
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