In LLMs, a token is the basic unit of text the model processes — roughly ¾ of a word in English. "SaaSTweaks" might split into 3 tokens; "the" is usually 1. Token count determines both context window size and API pricing, since most LLM APIs charge per input token and output token separately.
Efficient token usage — compressing context, truncating conversation history, caching repeated system prompts — is a practical cost-optimization skill for teams building LLM applications. Long documents, verbose prompts, or deep conversational history all consume tokens quickly. Prompt caching (supported by Anthropic and OpenAI) dramatically reduces costs for repeated system prompts.