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Plain-language AI glossary

Term 37InfrastructureMeaning / context / connections

Infrastructure / Definition

Distillation

A technique where a smaller "student" model is trained to replicate the behavior of a larger "teacher" model, achieving comparable quality at lower cost.

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MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

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UsesWhere the concept becomes useful.

01 / Plain-language context

How Distillation works.

Knowledge distillation trains a smaller student model to imitate useful behavior from a larger teacher model. The teacher generates targets, scores, or intermediate signals, and the student learns to reproduce them with less compute.

For builders, distillation can reduce serving cost or enable deployment on smaller hardware, but the result must be evaluated on the real task. A student may preserve common behavior while losing rare skills, calibration, or safety properties. A sound workflow defines the evaluation set first, creates legally and contractually permitted training data, trains the student, and compares quality, latency, and cost before replacing the teacher.

02 / Practical uses

Where it helps.

  1. 01Reducing inference costs in production
  2. 02Creating task-specific compact models
  3. 03Mobile model optimization
  4. 04Building cheaper alternatives to large models
03 / Keep exploring
Fine-TuningThe process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.QuantizationA technique that reduces AI model size and memory requirements by using lower-precision numbers to represent model weights, trading a small accuracy loss for major efficiency gains.InferenceThe process of running a trained AI model to generate predictions or outputs from new inputs, as opposed to training the model.Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.