Vector Voyage: RAG Card Quest – Game Instructions
Game Name: Vector Voyage: RAG Card Quest
Game Description:
Vector Voyage: RAG Card Quest is an immersive, low-tech card game for ages 12+ that takes you on a thrilling journey through the world of Large Language Models (LLMs). Players embark on a quest to chart a ‘vector map’ (knowledge space) by deploying sentence cards to forge paths between words, mimicking AI training. Brace for epic ‘hallucinations’—wild detours like a cow devouring fish!—as you challenge your map with query quests. Then, summon ‘RAG reinforcements’ (external cards) for Retrieval-Augmented Generation, merging maps to conquer errors and unlock accurate discoveries. Perfect for resource-limited settings: just print cards and grids! Designed for 2-6 adventurers or teams, 30-45 minutes, blending cooperative strategy with lessons on AI concepts like biases, prompts, and ethical tech in an adventurous, quest-driven format.
Objective of the Game:
Embark on a quest to understand how LLMs work! Build a “vector map” with words and connections from sentences. Experience “hallucinations” (wrong connections) and use RAG to fix them by combining external knowledge. Win by correcting hallucinations as a group and “unlocking” correct answers.
Game Rules (Step by Step):
- Setup (5 min):
- Each player gets 5 base sentence cards.
- Draw an empty 5×5 grid in the center (vector map).
- Set aside the RAG cards and query cards.
- Phase 1: Training – Build the Base Vector Space (15-20 min, 25 Epochs/Rounds):
- Goal: Simulate LLM training by creating a vector space with relations from sentences.
- Turns: Draw a sentence card (e.g., “cow is herbivore”). Split the words and place them on the grid (freely, but near related words). Draw lines between words in the sentence (neighborhoods).
- Repeat 25 times (speed up: play in batches of 5 rounds). Skip stopwords like “is” for connections.
- Educational: Discuss: “See how relations form? This is like embeddings in LLMs – words close if they often appear together.”
- Demo Phase: Test the Base (5 min):
- Draw a query card (e.g., “What does a cow eat?”).
- Trace a path on the grid (e.g., cow → drinks → water → fish). Discuss the output as a group: If the path is absurd or illogical (e.g., “Cow eats fish?”), it’s a hallucination.
- Educational: “This is a hallucination – the AI makes up wrong connections due to limited data.”
- Phase 2: RAG – Combine with External Knowledge (10 min):
- Goal: Fix hallucinations without retraining the base.
- Build a separate 3×3 mini-grid with RAG cards (external sentences, e.g., “herbivore eats grass”).
- Combination: Draw temporary lines from base to RAG where overlaps exist (e.g., “herbivore” in base links to “eats grass” in RAG). Base stays unchanged!
- Repeat the query: Trace a new path in the combination (e.g., cow → herbivore → eats → grass). Discuss the improved output as a group: If logical, it’s correct!
- Educational: “RAG retrieves external info and combines it – hallucinations gone, answers improved!”
- End :
- Discussion Round (5 min): “What did you learn about LLMs? How does RAG make AI smarter?”