Blog

Traditional RAG relies on similarity-based retrieval, but real-time performance optimization is the next frontier. In this blog, we explore how KPI-driven fine-tuning transforms RAG from static retrieval to a continuous optimization engine, ensuring LLMs always generate the most accurate, context-aware responses. Learn how real-time retrieval adjustments, utility-based ranking, and adaptive prompt composition enhance accuracy, reduce hallucinations, and eliminate costly fine-tuning cycles—without modifying model weights.