Deepmind expert says trimming documents improves accuracy despite large context windows
How useful are million-token context windows, really? In a recent interview, Nikolay Savinov from Deepmind explained that when a model is fed many tokens, it has to distribute its attention across all of them. This means focusing more on one part of the context automatically leads to less attention for the rest. To get the best results, Savinov recommends including only the content that is truly relevant to the task.
I'm just talking about-- the current reality is like, if you want to make good use of it right now, then, well, let's be realistic.
Nikolay Savinov
Recent research supports this approach. In practice, this could mean cutting out unnecessary pages from a PDF before sending it to an AI model, even if the system can technically process the entire document at once.
AI News Without the Hype – Curated by Humans
Subscribe to THE DECODER for ad-free reading, a weekly AI newsletter, our exclusive "AI Radar" frontier report six times a year, full archive access, and access to our comment section.
Subscribe nowRead on for the full picture.
Subscribe for hype-free coverage.
- Full access to every article on THE DECODER
- No ads
- Join the comments and community discussions
- A weekly AI news recap via mail
- 6x/year: "AI Radar" — deep dives on the AI topics that matter most
- Daily AI news, always up to date
- Our full ten-year archive
- Covered by a team with 10+ years in AI