Imagine asking a computer complex questions about your data, like "Which product had the highest sales last quarter?" or "What's the average age of our customer base?" Traditionally, getting answers required knowing specialized query languages like SQL or SPARQL. But what if you could simply ask in plain English? That's the promise of question answering (QA) systems for structured data. However, existing tools struggle with handling different data sources (tables, knowledge graphs, etc.) and often give untrustworthy results. A new research paper introduces TrustUQA, a framework that aims to solve these problems. It uses a clever "condition graph" to represent diverse data types in a unified way, allowing you to query tables, knowledge graphs, and even temporal data all with the same system. This is like having a universal translator for your data! TrustUQA takes a two-level approach to querying. First, it uses a large language model (LLM) to understand your natural language questions. Then, it translates these into precise, executable queries using predefined rules. This makes sure the results are accurate and consistent with your data. There is also a dynamic demonstration feature. TrustUQA includes a component to retrieve similar examples from a training set, effectively demonstrating to the LLM how it should respond. Experiments on benchmarks show that TrustUQA outperforms current systems on complex queries, delivering much more accurate answers. It also shows potential for general question answering, even handling questions where the relevant data comes from *multiple* sources. This suggests a future where asking sophisticated questions about any kind of data becomes as simple as typing a search query. While TrustUQA's performance on real-world, messy datasets needs further investigation, it offers a compelling vision for the future of truly intelligent data understanding.