What is AI training data?
Short answer. AI training data is the set of examples a model learns from: text, images, audio, code or records of work, each showing the model what a good answer or a real task looks like. Early models learned mostly from public web text. Labs now also license private records from companies, because those show how real work is done step by step.
General information, not legal advice.
What a model learns from examples
A model is a large set of numbers adjusted until its outputs match the examples it is shown. Show it millions of support conversations and it learns how a question is diagnosed and closed. Show it engineering change requests and it learns how a design problem is raised, argued and resolved. The model keeps patterns, not the records themselves, though a well-known risk is that it repeats rare passages it saw many times.
The examples come in sets with different jobs. The training set is what the model learns from. A validation set is held back to tune it. A test set is kept apart entirely and used to measure how good the model is. The EU AI Act uses the same three names in Article 10, where it asks providers of high-risk systems to govern each set for relevance, representativeness and errors.
Kinds of training data
Pre-training data is the broad base: large volumes of text, code and media that teach general language and knowledge. Fine-tuning data is smaller and targeted, such as question and answer pairs written by experts. Preference data records which of two answers a person judged better. Agent data, sometimes called trajectories, records each step a person or program took to finish a task in real software.
Company records can feed several of these. A help desk archive is a source of question and answer pairs. A project tool with comments and status changes is a source of task histories. Email threads that end in a decision show reasoning that public text rarely captures.
Why labs now license company records
Public text is large but uneven. It is full of finished articles and short of the messy middle of work: the first wrong guess, the follow-up question, the correction from a senior colleague. Those are exactly what models need to act as assistants and agents inside a business, and they exist mostly in private systems.
Licensing also answers a legal question. The US Copyright Office has described how copying works to build a training set can implicate the rights of their owners, and it expects a licensing market to keep growing. A company that licenses its own records, with personal information removed, offers a set whose origin is documented.
What makes a set useful
Volume matters less than people expect. What raises value is history that runs for years, records linked across tools, the outcome written down, and work that few others can offer. A set with those traits, documented in a manifest and checked for rights, is what a lab can actually use.
Where training data comes from
There are four broad sources. Public material gathered from the open web. Collections a lab pays people to create, such as expert answers or graded comparisons. Records generated by models themselves. And private records licensed from organisations that produced them while doing their normal work.
The last source is the newest market. A company that has run its operations in software for years holds a history no one else can produce, and the license is how it decides who may use that history, under what limits and for what return.
Follow-up questions
Is training data the same as a database?
A database is where records are kept for running the business. Training data is a copy prepared for a model: exported, cleaned, stripped of personal details and documented, usually as files rather than live tables.
Does a model keep a copy of what it was trained on?
It keeps learned patterns rather than files, but models can sometimes reproduce passages they saw often. That is one reason personal information is removed before training rather than after.
Can a small company have useful training data?
It can if the records are deep and specific, but labs usually look for a team large enough that the history covers many people, customers and cases. Our fit check shows where a company stands.
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