Large Language Models (LLMs) are increasingly used to translate natural language into SQL queries for databases. But how can we be sure they're getting it right? A new research paper reveals a surprisingly simple yet powerful method for calibrating the confidence of LLM-generated SQL: just rescale the model's own probabilities. This straightforward technique, using established methods like temperature scaling and isotonic regression, outperforms more complex approaches like prompting the LLM for self-checks or verbal confidence estimates. The researchers tested this across multiple LLM architectures and popular Text-to-SQL benchmarks (Spider and BIRD) and found that using the full product of token probabilities provided the best calibration. This discovery offers a significant advantage in real-world applications. Imagine automatically filtering out low-confidence queries or knowing when to involve a human expert. This boosts efficiency and reduces the risk of errors from faulty SQL. While this research focuses on SQL, the core idea—leveraging the model's inherent probability estimates—could extend to other code generation tasks, opening new possibilities for reliable and efficient code creation using LLMs. However, researchers acknowledge that schema-level calibration still poses a challenge. Performance can vary significantly depending on the specific database schema. Future work will likely focus on addressing this challenge to create even more robust and adaptable calibration techniques.