A news summary based on the article body: Enterprises faced persistent accuracy issues with AI investments, as basic large language models hallucinated on domain-specific topics like UPI flows and compliance rules. Standard RAG systems improved grounding but produced outputs that were bloated, stylistically inconsistent, or missing the real intent. When measured with frameworks like Ragas, accuracy on complex queries hovered in the low fifties percent range. The friction became impossible to ignore after proper evaluations using frameworks that test factual faithfulness and domain alignment; on harder, context-rich queries in regulated environments, correctness sat around the low fifties percent. The turning point was the recognition that the model needed to be shaped by the company’s own history of good answers, approved language, and real decision patterns, moving beyond retrieval alone into deliberate fine-tuning on proprietary data while keeping retrieval in place for everything that changes.