Glossary Library

Technical GlossaryGenerative AI and LLM

Scaling Laws

A set of empirical regularities describing how performance changes as model size, data, and compute increase.

Scaling laws show that building foundation models is not just about making models bigger, but about balancing scale with data and compute. They help explain when increasing model size remains efficient and when cost starts to dominate. They are one of the core decision frameworks in strategic model design.

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Explore these concepts to continue your artificial intelligence journey.

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Self-Supervised Learning

An approach that enables strong representation learning by generating supervision signals from the internal structure of the data itself.

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Optimization

The process of systematically improving model parameters according to an objective function in order to increase performance.

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Gradient Descent

A fundamental optimization method that updates parameters in the opposite direction of the gradient to minimize a loss function.