What is the "lost in the middle" problem, and how do yo...

Language models don’t attend uniformly across a long context — research on this shows recall is strongest for content near the beginning and end of the window and weakest in the middle. In RAG terms, that means the single most relevant retrieved chunk can go effectively unused if it happens to land at position five out of ten.

The fix isn’t one trick but a few compounding ones: order chunks so the strongest matches sit at the edges of the context rather than buried mid-list; retrieve fewer but better chunks rather than padding the context with marginal ones; and use a cheap model to compress each chunk down to its load-bearing sentences before it reaches the generation model, which both shrinks the “middle” and raises its average relevance.

A good reranker does double duty here — better precision means less irrelevant filler occupying the middle positions in the first place, which is often a bigger lever than reordering alone.