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AI glossary

Inference

Inference is the stage where a trained AI model is actually used - the moment it takes new input and produces an answer or result.

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In this guide

  1. 1What Inference means
  2. 2Why Inference matters

What Inference means

AI has two main phases. Training is when the model learns from data, which happens once and is very intensive. Inference is everything after that: each time the finished model is asked to do something, it performs inference.

Every time you type a question into a chatbot and get a reply, that reply is the result of inference. The model is not learning from your message - it is applying what it already learned to produce your answer.

Why Inference matters

Inference is the part of AI you interact with and pay for when building tools. Understanding it helps you think clearly about speed and cost.

Inference is what runs every time your AI tool is used
Most AI service pricing is based on inference usage
It explains why bigger requests can be slower or cost more
Knowing it helps you design efficient, affordable automations

Frequently asked questions

Generally no. During inference the model applies what it already learned and does not permanently update itself. Learning happens earlier, in the separate training phase.

More AI terms

GPTA family of large language models - GPT stands for Generative Pre-trained Transformer.Supervised LearningA machine learning method where a model learns from examples that are already labeled with the correct answer.Unsupervised LearningA machine learning method where a model finds patterns in data that has no labels or correct answers.Reinforcement LearningA machine learning method where a model learns by trial and error, guided by rewards for good outcomes.APIA defined way for two software systems to talk to each other and share data or features.Workflow AutomationSetting up a series of tasks to run automatically by connecting your apps and tools together.

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