Most people using AI tools have the wrong mental model of how context windows work — and that misunderstanding leads to real, avoidable mistakes. This episode breaks down what's actually happening under the hood, what the research says about where AI attention degrades, and the one habit change that makes the biggest practical difference.

AI-generated (NotebookLM) audio overview. Source: HexLocal in-house research — Research - How Context Windows Actually Work (Explainer) - 2026-06-12 (Dr. Priya Nair). Primary external sources include Liu et al. "Lost in the Middle" paper, NVIDIA's RULER benchmark, Chroma's "Context Rot" technical report, and Vaswani et al. (2017) on attention mechanisms.

- Context windows are not memory — the model re-reads the entire conversation from scratch on every single turn

- Information outside the window isn't forgotten or deprioritized; it's simply never seen by the model at all

- The "lost in the middle" effect: attention is strong at the start and end of a window, and measurably weak in the middle

- Bigger context windows don't linearly mean better results — quality decay ("context rot") sets in well before the advertised limit

- Context windows and RAG (retrieval-augmented generation) solve related but distinct problems and are not interchangeable

- The most actionable change: curate what goes into your context and front-load the most important material