Training vs. Inference
TL;DR
Training = AI learns. Inference = AI works.
What does this mean?
During training, a model learns from data and builds its knowledge. During inference, it applies that knowledge to answer queries. Training happens once (or rarely); inference runs continuously.
How it works
Training: millions of texts are processed, the model adjusts its parameters. Inference: a user asks a question, the model generates a response in real time.
Example
GPT-4 was trained over months (training). When you ask ChatGPT a question, it uses that training to respond (inference).
Why it matters
Helps explain why AI models can be expensive to develop but relatively cheap to operate.
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