trivia-engine.com: Transforming Jeopardy! with AI

(Visit Site)[https://trivia-engine.com]

Business Overview

What It Is:
Trivia-engine.com is an API service that transforms over 200,000 Jeopardy! clues from J-Archive into engaging multiple-choice questions using AI technology. This project extends my earlier work (jService) by addressing the most frequent developer request: converting free-response questions into the more accessible multiple-choice format.

Why It Matters:
As a Jeopardy! enthusiast, I created the original jService API to share this rich content with developers. However, the direct question-answer format created implementation challenges for many applications. Multiple-choice significantly lowers the barrier to entry, making the content more versatile for games, educational tools, and entertainment applications.

Key Challenges & Solutions

Challenge 1: Quality Answers at Scale

Problem: Creating plausible but incorrect answer options for 200,000+ questions would require thousands of hours of expert work.

Solution: Developed a specialized AI transformation engine using a local LLM (qwq:32b) through Ollama. This approach allowed for batch processing with carefully engineered prompts that generate contextually appropriate wrong answers while maintaining the original difficulty level.

Challenge 2: Cost-Effective Processing

Problem: Commercial AI APIs would make processing the entire database prohibitively expensive.

Solution: Implemented a local model deployment that eliminated per-token costs, enabling full database transformation with minimal ongoing expenses. This approach also reduced latency and eliminated external API dependencies.

Challenge 3: Maintaining Educational Value

Problem: Multiple-choice questions can become too easy if wrong answers are obviously incorrect, or too confusing if they're misleadingly similar to the right answer. Maintaining the perfect difficulty balance that makes Jeopardy! engaging was essential.

Solution: Developed sophisticated prompt engineering that analyzes the original clue's dollar value and category context to generate appropriate distractors. The system creates wrong answers that are thematically related but unambiguously incorrect, preserving the educational challenge that makes the content valuable. Each transformation undergoes validation to ensure it maintains appropriate difficulty without becoming misleading.

Technical Implementation

Technical Architecture Details

1. Data Foundation

• Extraction Pipeline: Node.js with Cheerio for efficient HTML parsing
• Database Design: PostgreSQL with Prisma ORM implementing relational models for Categories, Clues, Games, and Players
• Data Processing: Implemented cleansing to remove extraneous content and deduplication using game IDs and question-answer pairs

2. AI Transformation Engine

• Model Implementation: Deployed Ollama with qwq:32b model for high-quality local processing
• Prompt Engineering: Created structured templates that instruct the model to:
  • Rephrase clues into natural-language questions
  • Generate three plausible but definitively incorrect answers
  • Maintain consistent difficulty and knowledge domain
• Quality Control: Implemented JSON schema validation and error fallbacks to ensure consistent outputs

3. API Service Layer

• Framework: Express.js with modular controllers for users, API keys, and trivia content
• Authentication: Dual-layer system with JWT tokens for account access and API keys for service integration
• Performance: Database indexing and query optimization for efficient random selection and filtering

Technical Workflow Example

Here's how a typical transformation flows through the system:

1. Original Jeopardy! Clue Selection:

{
  "category": "WORLD GEOGRAPHY",
  "value": 800,
  "clue": "This African nation is the continent's largest producer of oil",
  "answer": "Nigeria",
  "gameId": 4680
}

2. AI Transformation Process:

// Simplified transformation prompt
const prompt = `
Convert this Jeopardy! clue into a multiple-choice question with one correct and three plausible but incorrect answers.

CATEGORY: ${clue.category}
CLUE: ${clue.clue}
CORRECT ANSWER: ${clue.answer}
DIFFICULTY: ${clue.value / 200} (on scale of 1-5)

Return a JSON object with:
1. A rephrased question in natural language
2. Four answer options including the correct one
3. The index of the correct answer
`;

const transformedQuestion = await ollama.generate({
  model: 'qwq:32b',
  prompt: prompt,
  format: 'json'
});

3. Transformed Result:

{
  "question": "Which African nation is the continent's largest producer of oil?",
  "options": [
    "Nigeria",
    "Angola",
    "Algeria",
    "Libya"
  ],
  "correctIndex": 0,
  "originalClue": {
    "id": 34592,
    "category": "WORLD GEOGRAPHY",
    "value": 800
  }
}

4. API Response:

{
  "success": true,
  "trivia": {
    "id": 12485,
    "question": "Which African nation is the continent's largest producer of oil?",
    "options": ["Nigeria", "Angola", "Algeria", "Libya"],
    "correctIndex": 0,
    "category": {
      "id": 583,
      "name": "WORLD GEOGRAPHY"
    },
    "difficulty": 4,
    "original": {
      "clueId": 34592,
      "gameId": 4680
    }
  }
}

Business Impact

Trivia-engine.com has transformed the original jService content into a more developer-friendly and accessible format that:

1. Expands Use Cases: Enables implementation in casual games, educational applications, and entertainment platforms
2. Increases Engagement: Multiple-choice format lowers the barrier to participation while maintaining the educational value
3. Provides Sustainable Value: The architecture supports scaling with minimal operational costs

Why It Works

The success of trivia-engine.com demonstrates how AI can transform existing content to add substantial value while maintaining its core quality. The project strikes an optimal balance between:

• Human Expertise: Using high-quality, curated Jeopardy! content as the foundation
• AI Transformation: Applying machine learning to handle the repetitive task of creating plausible alternatives
• Developer Experience: Delivering the content through a professionally designed API service

Rather than using AI to generate content from scratch, trivia-engine.com shows how AI can be most effectively used as a transformation layer that enhances existing expert-created content, making specialized knowledge more accessible to broader audiences.

Visit trivia-engine.com to explore the API and see how this approach could be applied to your own content transformation needs.