Key takeaways
- Training changes a model's weights; giving it information in a chat does not.
- Fine-tuning is further training on a narrower set of examples.
- Training, validation and test data are kept apart so a model is judged on cases it never saw.
- Buyers ask whether their data trains a model because trained-in information could surface for other customers.
What makes good training data
Good training data is accurate, representative of the cases the model will meet and labelled consistently.
A model copies what it sees. If deal records are half filled in, if one region dominates or if two people labelled the same kind of reply differently, the model learns those flaws. Size matters, but size alone does not fix gaps or bias.
Training data vs test data
Training data teaches the model, while test data is held back to check how well it works on cases it has never seen.
Teams usually split their examples three ways: a training set the model learns from, a validation set used to tune it and a test set kept aside for a final check. If test cases leak into training, the model looks better than it is, because it is being marked on answers it has already seen.
Training, fine-tuning and giving a model context
Training builds knowledge into a model's weights, fine-tuning adjusts those weights with narrower data, and giving a model context changes nothing inside it.
This difference is why many business tools give models your information as context or through retrieval rather than training on it: the information can be updated, removed or kept to one customer without changing the model.
- Training: learning from a large body of data, which sets the weights; done by the model's builder.
- Fine-tuning: further training on a smaller, focused set, such as one company's support tickets, which also changes the weights.
- Context: placing documents, notes or facts in the conversation, or fetching them at question time, so the model uses them for that task while its weights stay the same.
Does AI train on my data
Whether AI trains on your data depends on the product and its terms, so it is a question to ask every vendor directly.
The concern is reasonable. Information built into a model's weights is hard to remove cleanly afterwards, and in a shared model it could in principle surface in another customer's answers. In an illustrative case, a sales team pastes its pricing sheet into an AI tool and asks whether that pricing could ever appear for someone else.
- Is customer data used to train or fine-tune any model?
- Is each customer's data kept separate from every other customer's?
- Where is the data stored, and who can see it?
- What happens to the data when you leave?
Where Pebbles Ai fits
- FoundationAgent memoryUnder Tenant-Isolated AI, the Pebbles Ai architecture for tenant isolation and data security, each business works in its own tenant with its own data, memory and assistants, nothing crosses between tenants and no customer's data trains a model.
- FoundationCompany IntelPebbles Ai learns your company as you work: Company Intel holds your business knowledge and brand voice, and Agent memory keeps short facts you state or confirm.
Common questions
What is fine-tuning in AI?
Fine-tuning is extra training of an existing model on a smaller, focused dataset so it handles a narrower task or style better. It changes the model's weights, unlike giving it context in a chat.
Where does the training data for language models come from?
Mostly from very large collections of text, such as public web pages, books and code, plus data licensed or written for the purpose. Builders describe their sources in varying detail.
Can a model forget its training data?
Not easily. Removing what a model learned from particular data usually means retraining it, which is one reason buyers prefer tools that keep their data out of training.
About this page. Written by the Pebbles Ai team for people who run sales, marketing and growth. Last updated .
