queries/workshop_queries.json

{
  "schema_version": "1.0",
  "description": "Workshop driving queries — used by notebooks 1-8 for retrieval evaluation, ground-truth comparison, and CRAG gap demonstration.",
  "ground_truth_status": "Initial estimates. expected_relevant_doc_ids for queries B/C/E will be refined after Phase 2 (L1 build) once article content is known. D/G/H depend on L3 persona/conversation files written in Phase 5.",
  "queries": [
    {
      "id": "A",
      "text": "What's there to do in Iceland?",
      "used_in_notebooks": [1, 2, 3, 4, 7],
      "expected_relevant_doc_ids": [
        "iceland",
        "reykjavik",
        "vatnajokull",
        "iceland-westfjords"
      ],
      "expected_winning_pipeline": "basic",
      "narrative_role": "Easy baseline — naive RAG already works on a single, clearly-named destination. Establishes that the system is alive.",
      "data_dependencies": ["L1.articles", "L1.chunks"]
    },
    {
      "id": "B",
      "text": "Affordable destinations in Southeast Asia for snorkeling and vegetarian food in March",
      "used_in_notebooks": [1, 2, 3, 4, 5, 7],
      "expected_relevant_doc_ids": [
        "thailand",
        "vietnam",
        "malaysia",
        "philippines",
        "indonesia",
        "cambodia",
        "bali",
        "ubud",
        "phuket",
        "koh-phi-phi",
        "koh-samui",
        "palawan",
        "boracay",
        "langkawi",
        "gili-islands",
        "khao-sok"
      ],
      "expected_winning_pipeline": "graphrag",
      "narrative_role": "Hard multi-aspect — combines geography (Southeast Asia), price (affordable), activity (snorkeling), cuisine (vegetarian), and season (March). Naive RAG returns shallow results; each notebook recovers more relevant docs. Main motivator for the entire arc.",
      "data_dependencies": [
        "L1.articles",
        "L1.chunks",
        "L1.metadata",
        "L2.pricing",
        "L2.seasonal",
        "RAG.Entities",
        "RAG.EntityRelationships"
      ]
    },
    {
      "id": "C",
      "text": "Where can I go for great hiking and excellent local food?",
      "used_in_notebooks": [6],
      "expected_relevant_doc_ids": [
        "peru",
        "sacred-valley",
        "japan",
        "japanese-alps",
        "nepal",
        "sagarmatha",
        "tuscany",
        "andalusia",
        "dolomites",
        "switzerland",
        "patagonia",
        "scottish-highlands",
        "morocco",
        "vietnam",
        "bhutan"
      ],
      "expected_winning_pipeline": "crag",
      "narrative_role": "Mid-confidence ambiguous — CRAG's evaluator detects partial coverage and triggers enhanced retrieval (query rewriting + extra rounds) before answering. Contrasts with F (where CRAG refuses) and B (where graphrag wins outright).",
      "data_dependencies": ["L1.articles", "L1.chunks", "L2.tips"]
    },
    {
      "id": "D",
      "text": "What did I love most about my Bali trip?",
      "used_in_notebooks": [8],
      "expected_relevant_doc_ids": ["bali", "ubud"],
      "expected_relevant_persona_turns": "TODO — fill once L3 conversation seeds are written for persona 'me' in Phase 5",
      "expected_winning_pipeline": "basic",
      "narrative_role": "Personal-corpus retrieval — answer comes primarily from the attendee's prior conversation history, with destination chunks as supporting context.",
      "data_dependencies": ["L1.articles", "L3.persona", "L3.conversations"]
    },
    {
      "id": "E",
      "text": "Find diving spots similar to the Great Barrier Reef",
      "used_in_notebooks": [5, 7],
      "expected_relevant_doc_ids": [
        "great-barrier-reef",
        "maldives",
        "palawan",
        "koh-phi-phi",
        "okinawa",
        "fiji",
        "bora-bora",
        "tahiti",
        "galapagos",
        "fernando-de-noronha",
        "andaman-islands",
        "komodo"
      ],
      "expected_winning_pipeline": "graphrag",
      "narrative_role": "Similar-to via graph traversal — uses entity relationships (same_type, near) that vector search alone misses. Vector retrieval may surface scuba/marine text but lacks the explicit 'reef destination' link.",
      "data_dependencies": [
        "L1.articles",
        "L1.chunks",
        "RAG.Entities",
        "RAG.EntityRelationships"
      ]
    },
    {
      "id": "F",
      "text": "What does it cost to travel in New Zealand in October?",
      "used_in_notebooks": [6, 7],
      "expected_relevant_doc_ids": [],
      "expected_winning_pipeline": "crag",
      "narrative_role": "Deliberate corpus gap — NZ excluded from L1, L2, and personas. basic retrieves nearest-neighbor noise (Australia, Tasmania, Tahiti) and the LLM fabricates a confident-sounding answer. CRAG's evaluator classifies disoriented and refuses. Side-by-side comparison is the hallucination-defense punchline.",
      "data_dependencies": ["L1.articles", "L1.chunks"],
      "is_corpus_gap": true
    },
    {
      "id": "G",
      "text": "Find a place that works for me and Sarah",
      "used_in_notebooks": [8],
      "expected_relevant_doc_ids": "TODO — depends on the intersection of 'me' persona and 'Sarah' persona compatible_with fields; populate once Phase 5 personas are written",
      "expected_winning_pipeline": "basic_rerank",
      "narrative_role": "Multi-persona intersection — merges 'me' and 'Sarah' preference profiles, reranks destinations against both compatible_with constraints. Demonstrates persona-as-filter.",
      "data_dependencies": ["L1.articles", "L3.persona.me", "L3.persona.sarah"]
    },
    {
      "id": "H",
      "text": "Remind me what I told you about my dietary restrictions",
      "used_in_notebooks": [8],
      "expected_relevant_doc_ids": [],
      "expected_relevant_persona_turns": "TODO — fill once L3 conversation seeds include dietary references for persona 'me' in Phase 5",
      "expected_winning_pipeline": "basic",
      "narrative_role": "Memory-only retrieval — answer comes purely from prior conversation turns. No L1 docs are relevant. Demonstrates that RAG over personal text is the same primitive as RAG over a document corpus.",
      "data_dependencies": ["L3.persona", "L3.conversations"]
    }
  ]
}