In plain terms
A model is a reflection of what it was fed. If the data is rich in a subject, the model is strong there; if a language or a viewpoint is rare in the data, the model is weaker. Errors and prejudices in the data come along too. “Garbage in, garbage out” applies at a scale no one can fully inspect.
Why it matters
Most legal, ethical and quality questions about AI trace back to training data: copyright disputes, bias, privacy, and why a model is weaker in Turkish than in English. When choosing a vendor, the relevant questions are whether your own inputs are used for training, under what terms, and how the provider sourced its data.
Example
A model answers confidently about US employment law and vaguely about Turkish labour regulations. Nothing is wrong with the model's reasoning: English-language legal text is abundant on the web, Turkish legal text far less so. The fix is to supply the Turkish sources at query time through retrieval.
Most often confused with
Training Data vs. Context
Training data shaped the weights before the model was released and cannot be consulted or corrected afterwards. Context is the material you provide at the moment of asking. To give a model your company's current information you put it in the context; you do not retrain.
Under the hood
Typical sources: public web crawls, books and papers, code repositories, licensed archives, human-written demonstrations and, increasingly, synthetic data generated by other models. Processing includes deduplication, quality and safety filtering, removal of personal data, and mixing ratios between sources. Known issues: memorisation of passages that appear often, under-representation of languages and groups, contamination of benchmarks, and legal uncertainty over copyrighted material. Enterprise API terms commonly exclude customer data from training by default; consumer products often differ, so read the terms.