Learn AI

Open and closed AI models differ in access, control, deployment, customization, and responsibility. Learn the tradeoffs before choosing a model strategy.

Small language models trade some scale for speed, efficiency, lower hardware requirements, and easier local deployment. Learn when smaller AI is the better choice.

AI models can contain millions or billions of learned parameters. Learn what parameters are, how training changes them, and why more parameters do not always mean a better model.

AI memory is usually a product feature built around storage and retrieval, not a human-like ability inside the model. Learn how short-term and long-term memory work.

Reasoning-focused AI models are designed to spend more computation on difficult tasks. Learn how they differ from ordinary generation and where they help.

Embeddings turn words, documents, images, and other data into numerical representations that capture useful relationships. Learn why they power modern AI search.

Fine-tuning adapts a pretrained AI model using task-specific examples. Learn what changes, when fine-tuning helps, and when another approach is better.

RAG helps AI answer questions using external documents and knowledge sources. Learn how retrieval, embeddings, and generation work together.

An AI context window limits how much information a model can use at one time. Learn how context works, why long chats lose details, and how to manage it.

AI models process text as tokens rather than ordinary words. Learn what tokens are, how they affect context and cost, and why token limits matter.