Dimensions overall score 8.0

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• The two-phase supervised fine-tuning method enables a model to internalize an entire database schema, eliminating the need for long-context prompts.method 90%
• The method reduces input tokens by over 99%, from a 17k-token baseline to fewer than 100 tokens.result 95%
• The specialized 8B-parameter model achieves 98.4% execution success and 92.5% semantic accuracy on text-to-SQL tasks.result 95%
• The system substantially outperforms a prompt-engineered baseline using Google's Gemini Flash 2.0 (95.6% execution, 89.4% semantic accuracy).result 90%
• The approach replaces costly external API calls with efficient local inference for text-to-SQL tasks.technical 85%
• Relying on massive, schema-heavy prompts with proprietary API-based models results in prohibitive per-token API costs and high latency for text-to-SQL tasks.limitation 80%
• The model was designed for a conversational bot in CriQ, a sister app to Dream11, India's largest fantasy sports platform with over 250 million users.market 90%
• The approach demonstrates a practical path toward high-precision, low-latency text-to-SQL applications using domain-specialized, self-hosted language models in large-scale production environments.technical 75%

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