Context windows in AI: why every token is a budget decision

Workers AI - Workers AI larger context windows

Token Budgeting: How Context Engineering Can Slash Your LLM Costs

This article debunks the common misconception that token optimization for LLMs is simply about writing shorter prompts. It reframes token op

Impact of Increasing Input Tokens on LLM Performance

Recent developments in large language models (LLMs) are focusing on longer context windows with millions of input tokens. The assumption tha

LLM context windows have a "smart zone" and "dumb zone" — coding agents push you into the latter

The article discusses the concept of LLM context windows having two zones: a "smart zone" where the model performs well (up to ~100k tokens)

Subquadratic's 12 Million Token AI: What It Means for Australian Businesses

Strategies for Mitigating Context Failures in LLM Applications

This article provides practical strategies for mitigating and avoiding context failures in large language model applications, focusing on in