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Inference is the process of using a trained AI model to make predictions or generate outputs on new, unseen data. While training is about learning patterns, inference is about applying what the model has learned to real-world inputs.
Inference speed and cost determine whether an AI model is practical for production use — a brilliant model is useless if it takes too long or costs too much to run.
When you ask ChatGPT a question, the model performs inference in real time — processing your input and generating a response in seconds.
Model
In AI, a model is the mathematical representation that a machine learning system builds from training data. It captures the patterns, relationships, and rules discovered during training and uses them to make predictions or generate outputs on new data.
API (Application Programming Interface)
An API is a set of rules and protocols that allows different software applications to communicate with each other. In AI, APIs let developers integrate AI capabilities — like text generation or image analysis — into their own applications without building models from scratch.
Large Language Model (LLM)
A large language model is an AI system trained on vast quantities of text data that can understand, generate, and reason about human language. LLMs use the transformer architecture and contain billions of parameters, enabling them to perform a wide range of language tasks.
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