AI Key Terms Glossary

Core Vocabulary

These terms show up constantly in research papers, product documentation, and everyday conversations about AI. Knowing them precisely makes it much easier to follow more advanced material without getting lost in jargon.

Digging a Bit Deeper

A model and its parameters are closely linked but not the same thing: the model is the overall structure and behavior, while the parameters are the specific numbers learned during training that make one trained model different from another with the same structure.

Training, fine-tuning, and inference are three distinct phases in a model's lifecycle, and it helps to keep them clearly separated.

• Training happens first, often over days or weeks, using large general-purpose datasets to build a broadly capable base model.
• Fine-tuning happens afterward, usually much faster, to adapt that base model toward a specific task, style, or domain.
• Inference happens continuously after deployment, every single time a user sends a prompt and receives a response.

Context Window and Latency in Practice

A context window acts like a limited working memory: once a conversation or document exceeds it, earlier content can no longer be directly considered by the model when forming a response, even if it was part of the same session.