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How ZURU improved the accuracy of floor plan generation by 109% using Amazon Bedrock and Amazon SageMaker
- ZURU Tech developed Dreamcatcher, a user-friendly platform for building design and construction collaboration.
- They collaborated with AWS to create a more accurate text-to-floor plan generator using generative AI.
- ZURU's evaluation framework ensured accuracy in generating 2D floor plans based on user prompts.
- They found success using a GPT2 LLM approach for accurate floor plan generation.
- ZURU employed prompt engineering and fine-tuning with Llama 3B variants to improve model accuracy.
- Dataset preparation involved gathering floor plans and streamlining the review process using a custom application.
- Dynamic few-shot prompting and prompt decomposition methods enhanced the relevancy and quality of generated content.
- The workflow involved using Amazon Bedrock and Amazon SageMaker for AI model optimization.
- Fine-tuning approaches included full parameter fine-tuning and Low-Rank Adaptation for optimized performance.
- The evaluation framework compared different approaches, with prompt engineering and full fine-tuning showing improved accuracy over baseline models.
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