How do you counter 'lost in the middle' in RAG?
mediumAnswer
- (1) Fewer, better chunks: retrieve top-5 with a reranker instead of stuffing top-20.
- (2) Reorder retrieved docs so most relevant are at the start / end.
- (3) Split into multiple LLM calls, each with a small context, then aggregate.
- (4) 'Attention sink' prompts that summarize the middle.
- (5) Use models with better long-context tuning (Claude 3, GPT-4.1, Gemini 1.5).
- Measure with 'needle in a haystack' evaluations across your target context length.
Check yourself — multiple choice
- Ignore the phenomenon
- Fewer better chunks (rerank), reorder to edges, split into calls + aggregate, use long-context-tuned models; measure via NIAH
- Only bigger models
- Impossible to fix
Fix lost-in-middle: rerank / reorder / split / long-ctx tuned models + NIAH eval.
#rag#long-context
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