AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
Training data is the collection of examples an AI model learns from, and its quality sets the ceiling on how well the model can perform. Most AI project delays trace back to data that is missing, messy, or locked in systems that do not talk to each other. Budget for data work before model work.
Training data is the school curriculum. If the curriculum is biased, outdated, or incomplete, the graduate will be too, no matter how smart they are.
A healthcare startup spent three months sourcing and cleaning labeled medical transcripts before touching a model. That work directly determined the ceiling on what the model could achieve.
Every supervised AI project needs labeled data. Invest in data quality before model selection. The best model trained on bad data will underperform a simple model trained on good data.
You cannot skip data preparation. If you do not have sufficient, representative, and clean historical data for the task, no model architecture will compensate for that gap.
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