Case Study

Production RAG Eval – Enterprise Retrieval-Augmented AI Assistant

Production RAG Eval is an enterprise-grade Retrieval-Augmented Generation (RAG) platform designed to deliver reliable, context-aware AI responses with measurable quality. Unlike traditional chatbots, this system combines semantic search, intelligent document retrieval, and automated evaluation pipelines to ensure every generated response is accurate, grounded, and production-ready. The platform leverages FastAPI for backend services, ChromaDB for high-performance vector storage, and LangChain to orchestrate retrieval workflows. It integrates Google Gemini and local Ollama models, allowing flexible deployment while optimizing operational costs. Advanced retrieval strategies, including Maximal Marginal Relevance (MMR), improve contextual relevance by selecting diverse yet highly related documents before generation. To maintain production quality, an automated evaluation framework powered by RAGAS and DeepEval continuously measures answer faithfulness, contextual precision, and response relevance within a GitHub Actions CI pipeline. This evaluation-first architecture enables continuous improvement, significantly reducing hallucinations while maintaining low response latency. Designed with scalability, maintainability, and enterprise AI best practices in mind, the project demonstrates how modern LLM systems can be deployed with confidence through measurable performance, automated testing, and robust engineering practices.