Inference: using a trained model
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Sources counted: 5
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Open in the glossary Reading notes · Structured record
In plain language
Running a trained model on an input to produce an output. [1]
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Limits & distinctions
Using a detail you supplied in a conversation does not, by itself, mean the model's weights were retrained. That detail can be used as part of the current input. [5]
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A fuller explanation
When a chatbot generates a reply, it performs inference. The model applies its learned weights to the current input. In text generation, the process repeats as tokens are added to the answer. [2] [3]
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How it relates to the map
This separates the work of answering a request from the work of training model weights. [1] [4]
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Sources and what we read
1. Machine Learning Glossary
Publication dates and source age
Sources counted: 1
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read the artificial intelligence, deep model, context window, inference and chat entries. Used for terminology, not product performance claims. Updated date follows the earlier displayed page date; initial publication is unspecified. The chat entry was reread during the same-day beginner-content review. Also read the generalization and compute entries.
2. Models
Publication dates and source age
Sources counted: 1
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read architecture, weights, checkpoints, loading and saving. Used to distinguish a model's structure and learned values from the application around it. Example code was read, not executed; live page publication date unspecified.
3. Text generation
Publication dates and source age
Sources counted: 1
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read next-token generation, generation settings, temperature, sampling and prompt-format sections. Library options illustrate the process; defaults and suggested temperatures are not treated as universal chatbot behavior. Live page publication date unspecified.
4. Linear regression: Gradient descent
Publication dates and source age
Sources counted: 1
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read the iterative prediction, loss and parameter-update explanation. The page's guarantees for convex linear regression are not extended here to neural-network training. Updated date follows the page; initial publication is unspecified.
5. LLMs: Fine-tuning, distillation, and prompt engineering
Publication dates and source age
Sources counted: 1
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read fine-tuning and prompt engineering, including the distinction between parameter updates and examples supplied as input. Used to distinguish these processes, without adopting general claims that fine-tuning is always necessary or improves every task.
Edition and machine-readable evidence
Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.
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