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factual: \"[percentage] — direct fact lookup\"\n    - analytical: \"[percentage] — synthesis across documents\"\n    - procedural: \"[percentage] — how-to, step-by-step\"\n    - comparative: \"[percentage] — compare X vs Y\"\n    - conversational: \"[percentage] — multi-turn follow-ups\"\n\n  # What data do we have?\n  corpus:\n    total_documents: \"[count]\"\n    total_size: \"[GB/TB]\"\n    document_types:\n      - type: \"[PDF/HTML/markdown/code/JSON/CSV]\"\n        count: \"[count]\"\n        avg_length: \"[pages/tokens]\"\n    update_frequency: \"[static / daily / real-time]\"\n    languages: [\"en\", \"...\"]\n    quality: \"[curated / mixed / noisy]\"\n\n  # Requirements\n  accuracy_target: \"[% — start with 85%]\"\n  latency_target: \"[ms P95]\"\n    max_cost_per_query: \"[$]\"\n  scale: \"[queries/day]\"\n  multi_turn: \"[yes/no]\"\n  citations_required: \"[yes/no]\"\n\n  # Constraints\n  deployment: \"[cloud / on-prem / hybrid]\"\n  data_sensitivity: \"[public / internal / PII / regulated]\"\n  budget: \"[$/month for infrastructure]\""},{"kind":"example","language":"text","snippet":"Is your corpus < 100 documents AND < 50 pages each?\n├─ YES → Consider full-context stuffing (no RAG needed)\n│        Use: Long-context model (Gemini 1M, Claude 200K)\n│        When: Static docs, low query volume, budget allows\n│\n└─ NO → RAG is appropriate\n         │\n         Is real-time freshness critical?\n         ├─ YES → Streaming RAG with incremental indexing\n         └─ NO → Batch-indexed RAG\n                  │\n                  Do queries need multi-step reasoning?\n                  ├─ YES → Agentic RAG (query planning + tool use)\n                  └─ NO → Standard retrieval pipeline\n                           │\n                           Single document type?\n                           ├─ YES → Single-index RAG\n                           └─ NO → Multi-index with routing"}]}}