Long-Context Models: The Window Is Wide Open, But Mind What You Put In
Practical lessons from working with million-token context windows — and why context design still matters enormously.
I spent most of this week experimenting with long-context models — systems that can take in hundreds of thousands, or in some cases over a million, tokens in a single pass. The promise is obvious: just throw the whole document, codebase, or knowledge base into the context and let the model figure it out. So I tested that assumption as directly as I could.
I fed in large documents and asked questions that required synthesizing information scattered across the full text. For some tasks it worked impressively well. But I kept bumping into a pattern I had read about but not fully appreciated until I saw it myself — when the most relevant information was buried somewhere in the middle of a very long context, answer quality dropped noticeably. The model seemed to weight content near the beginning and end of the window more reliably than content in the middle. Longer contexts also come with real compute costs that add up fast for high-throughput applications.
My takeaway from this week is that long-context models are a genuinely useful addition to the toolkit, but they do not make context design irrelevant. If anything, they raise the stakes. Knowing what to put in the context, in what order, and what to leave out is still as important as ever — maybe more so, because the failure modes are now subtler and harder to spot. Smart context management is the new prompt engineering, and I am planning to spend more time on it in the weeks ahead.
