Data PROMPT
Retrieval vs Long Context Decision
July 26, 2026Optimized for: anyRAG vs long context architecture
Help me decide whether to use retrieval or to stuff the context window for this workload. Be opinionated. Analyse: 1. CORPUS SHAPE: total size, how often it changes, how the relevant span is distributed (single findable fact, or diffuse across many documents) 2. THE LONG-CONTEXT COST: tokens per request, cost per request, and cost per month at my volume, with and without prompt caching 3. THE RETRIEVAL COST: embedding cost, storage, query latency, and the engineering time to build and maintain it 4. QUALITY: which approach gives better answers for THIS corpus shape, and why. Account for lost-in-the-middle degradation honestly. 5. VERDICT: one recommendation. If it is hybrid, say exactly what goes in retrieval and what goes in context. Do not hedge. Pick one. Corpus: [DESCRIBE] Volume: [REQUESTS PER DAY] Budget: [MONTHLY]
Forces a decision between RAG and context stuffing with the real cost arithmetic on both sides, including the lost-in-the-middle penalty.
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