A language model learns statistical patterns from an enormous body of text during training. The quality, variety and representativeness of that data largely determine what the model does well and where it is more likely to be wrong or imprecise.
What that means in practice
A model trained mainly on general text tends to be more reliable on general tasks — understanding language, writing, everyday reasoning — and less precise on very specific knowledge in niche fields, where the training data was probably thinner or less well represented.
Why context makes up for it
This is where the context supplied at the time of use becomes decisive: even a model with only general knowledge of a trade can reason correctly about it if it receives, at the moment of the request, the specific relevant data — real company parameters, sector figures, concrete examples — rather than having to "know" it from training.
A practical implication when choosing a tool
What matters is less knowing exactly what a model was trained on, and more checking whether the tool built around it supplies the specific context your trade needs. A good system compensates for the model's general limits with a targeted injection of specific knowledge at the right moment.