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Long-Context LLMs — Lost in the Middle, RAG vs. Natively Long, KV Cache & Packing
What 32K–1M+ token support really means: attention and KV cache economics, the lost-in-the-middle result (Liu et al., TACL 2024, arXiv:2307.03172), needle-in-haystack and chunk reordering for RAG, and when retrieval still wins on cost and proof. Ties the five GenAI planes for staff interviews.
Bigger context windows are a budget line item
Dense self-attention has O(L²) FLOP growth in the naïve accounting for each layer, and long prompts mean long prefill phases before the first new token. Flash Attention (Dao et al.) reduces HBM round-trips and the materialized n×n footprint; it does not make “infinite” context “free” in FLOPs or in wall-clock. At serving time, KV cache for prior tokens also grows with context length and often dominates GPU memory for long interactive chats.
Liu et al., Lost in the Middle (TACL 2024; arXiv:2307.03172) showed a U-shaped pattern: models often use evidence at the start or end of a long multi-doc prompt better than evidence in the middle on their controlled multi-doc QA and key–value retrieval settings. RAG systems should reorder packed chunks, tighten K after rerank, and not “paste the whole drive” as a product strategy.
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