FloTorch is an open source tool designed to streamline the proof of concept process for Generative AI or GenAI workloads. It allows customers to specify critical hyperparameters for RAG workloads and run the accelerator within their own AWS infrastructure, ensuring data security and sovereignty. FloTorch generates multiple experiments based on these hyperparameters, validates them through preflight checks, and provides directional estimates for costs, latency, and various evaluation metrics. Customers can then select and execute the most promising experiments, with results presented in a comprehensive dashboard. Key features of FloTorch include hyperparameter optimization, vector database enhancement, and LLM integration. The tool facilitates systematic experimentation with document segmentation strategies, embedding model selection, and retrieval strategy refinement. It supports the evaluation of various embedding models and vector database solutions, ensuring optimal storage and retrieval performance. Additionally, the accelerator enables finetuning of context management and LLM prompt engineering, helping organizations achieve the best balance between processing efficiency and context preservation. The benefits of using FloTorch are manifold. It provides automated benchmarking, quality validation, and cost efficiency through detailed resource analysis and return on investment evaluation. The platform ensures production readiness with rigorous validation protocols and comprehensive deployment blueprints. By leveraging AWS infrastructure as code capabilities, the accelerator reduces time to value for RAG implementations while maintaining security best practices. This makes it an invaluable tool for organizations looking to optimize their GenAI workloads in a secure, efficient, and cost effective manner.