Originally published on AI Tech Connect.
What builders need to know first Recall is not uniform. Models reliably retrieve content from the start and end of a context, but performance collapses for material placed in the middle — the "lost in the middle" effect first described by Liu et al. The threshold is lower than you think. The degradation is measurable from around 20k tokens and becomes operationally significant well before 200k. Prompt caching is the economic lever. On Claude Opus 4.7, cached input reads at $0.50/MTok versus $5/MTok fresh — a 10× cost saving that makes 1M-token sessions financially viable. Structure beats raw size. XML markers and explicit section anchors recover a significant fraction of the recall lost to position bias. Structural engineering should precede any decision to increase context size. Server-…
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