The blog post explores the challenges and strategies involved in converting natural language queries into structured query syntax for different types of data, including structured, semi-structured, and unstructured data. It highlights the concept of query construction, which translates user queries into appropriate database query languages, such as SQL for structured data and Cypher for graph databases. The post emphasizes the role of language models (LLMs) in improving retrieval-augmented generation by constructing precise queries that leverage both the structure of the data and semantic understanding. It discusses the challenges of hallucinations and user errors in text-to-SQL conversions and suggests methods to overcome them, such as providing accurate database descriptions and using few-shot learning. Additionally, it introduces the integration of vector support with relational databases, enabling hybrid retrieval approaches that combine semantic and structured searches. The article provides insights into the use of knowledge graphs for modeling complex relationships and mentions the potential of using advanced LLMs like GPT-4 for generating valid Cypher queries. Overall, the post underscores the importance of developing effective natural-language-to-structured query systems to maximize the capabilities of LLMs across various data sources.