Large language models (LLMs) are revolutionizing data analytics by allowing users to query data using natural language. However, these powerful AI tools sometimes “hallucinate,” generating inaccurate or entirely fabricated information. Imagine asking an AI about sales trends and receiving a detailed report on a product that doesn't exist! This poses a significant problem for data-driven decision-making. New research explores innovative techniques beyond traditional fine-tuning to tackle this challenge. Instead of simply tweaking the model's parameters, researchers are exploring methods like enforcing strict rules for data retrieval, enhancing prompts with contextual metadata, and integrating a semantic layer to improve data understanding. These methods act like guardrails, guiding the LLM to generate more accurate and reliable responses. For example, by requiring the LLM to produce structured code before providing a natural language answer, researchers can verify the AI’s reasoning process, reducing the risk of hallucinations. Early results show that these strategies are highly effective, significantly decreasing hallucination rates compared to traditional methods. This is a critical step toward making LLMs more trustworthy for data analysis and empowering users to glean accurate insights from their data without needing advanced technical skills. While further research is needed to optimize these methods and address computational challenges, the promise of more reliable AI-driven data analysis is within reach.