Training
By HubAI editorial team · Source checked: 24 October 2026What does it mean?
Adjusting a model’s parameters using data and an optimisation process. It is different from running the trained model to get an answer, which is inference.
Picture this
During training a model repeatedly compares predictions with training targets and adjusts weights. Later, an app uses the trained model to process a customer’s request.
Where you’ll come across it
Creating models and adapting them to particular tasks. For an ordinary app buyer, the important question is whether their data is used for training and under which settings.
What it doesn’t mean
Using a chatbot does not necessarily train it in real time. Data retention, product memory and provider training policies are separate issues that need separate checks.
Compare on a job, not a label
A short exercise · use fictional or non-sensitive material.
- Choose a small set of non-sensitive tasks, including an awkward case and a missing-information case.
- Keep the inputs and scoring checklist the same when you compare two model setups.
- Record useful completions, corrections and elapsed time. Keep the model name and settings with the results.
Is the improvement still useful after you include checking and correction time?
Did that make it clearer?
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Written and explained by HubAI. The reference below lets you go deeper; it is not an endorsement of a particular product.
Technical reference: developers.google.com ↗