Law 03 · Context & Reliability
Position Is Power
Models read the edges. The middle gets lost.
The principle
Give a model a long input and it pays the most attention to the start and the end. Facts buried in the middle quietly lose their grip. They're present but basically ignored. That's the worst kind of bug, because the information was technically in context and nothing looks wrong.
Why it happens
Long context is not uniform attention. Models tend to use the beginning and end of an input more reliably than the middle, and the dip is worse when the key fact has no exact keyword hook. Newer models reduce the effect, but they have not made it disappear. That makes the middle of a long prompt a dangerous hiding place for critical facts. The system does not error. The fact is technically present. It is simply less likely to shape the answer when the model needs it.
Watch for
- The agent misses a fact you can confirm is sitting in the middle of a long input.
- Accuracy on the same task degrades sharply as you lengthen the context.
- Reordering the input so the key fact is near the top or bottom suddenly fixes the answer.
In practice
You paste a 12-page contract into context and ask the agent to flag the termination clause, but it confidently misses the 90-day notice buried on page 7 because that clause sat dead-center in the input. Nothing errored; the fact was technically in context and still ignored. Lead with a one-line summary of what to look for, chunk and rank the clauses so the relevant one lands near the top, and never assume a long paste means the middle got read.
Apply it
- Lead with a short summary of what to find, and restate the critical instruction at the very end.
- Rank and place the most relevant retrieved passages at the edges of the context, not the middle.
- Test long-context retrieval with questions that have no keyword overlap, not just literal needle matches.
The takeaway
Put the most important instructions and findings at the top or the bottom. Lead with a summary, break things up with clear headers, and don't assume that 'in the context' means the model actually used it.