AI terms, explained
The concepts behind the prices and recommendations on this site — tokens, context windows, prompt caching, agents, and the rest of the jargon — in plain language.
Understanding AI Tokens
When managing or designing AI products, you'll constantly hear the term token. While humans think in words and sentences, AI models process…
Read →Architectural Model Routing: How to Build a Cascading LLM System
When teams first deploy AI features, they usually default to a single model. If they need high-quality reasoning, they hook their entire…
Read →The Multimodal Matrix: Comparing Text, Image, Audio, and Video Generation
When building general AI solutions, product teams often assume that moving from text to rich media (images, audio, and video) is just a…
Read →Understanding Prompt Caching: The Ultimate Cheat Code for AI Cost Reduction
If you are running a production AI application—especially a customer support chatbot, a coding assistant, or a RAG (Retrieval-Augmented…
Read →Understanding RAG (Retrieval-Augmented Generation)
When you deploy a standard large language model, it can only answer questions using the data it was trained on. It doesn't know who your…
Read →Understanding the Context Window (And What Happens When It's Full)
Every large language model has a hard physical limit on how much total text it can read, hold in memory, and generate at any single moment.…
Read →