Imagine an AI assistant that learns from its mistakes, just like a student diligently filling a notebook with corrected errors and insightful reflections. This isn't science fiction; it's the core idea behind a new research project exploring how to make AI systems dramatically better at understanding and interacting with databases. While Large Language Models (LLMs) have shown remarkable progress in generating human-like text, they often stumble when faced with the structured logic of databases, particularly when converting natural language questions into SQL queries (a task called Text-to-SQL). This research introduces LPE-SQL (Leveraging Prior Experience for Text-to-SQL), a framework that mimics human continual learning by keeping a 'correct notebook' and a 'mistake notebook.' Whenever the AI successfully generates a SQL query, its reasoning process is logged in the correct notebook. If the AI makes a mistake, the error, along with tips on how to avoid it in the future, is recorded in the mistake notebook. This allows the LLM to consult its past experiences, both good and bad, when tackling new queries. Testing this approach on the challenging BIRD benchmark dataset yielded promising results. The smaller Llama-3.1-70B model, enhanced with continual learning, actually outperformed the much larger Llama-3.1-405B using standard methods, showcasing the power of learning from experience. Interestingly, different LLMs exhibited varying learning patterns, with some models benefiting more from the 'correct notebook' and others learning better from their mistakes. This suggests that tailoring the learning strategy to the specific LLM could unlock even greater performance gains. The implications extend far beyond just database queries. This approach, grounded in continual learning, could significantly improve AI’s performance in a range of reasoning tasks, paving the way for more robust and adaptable AI assistants in the future.