Top 4 Challenges using RAG with LLMs to Query Database (Text-to-SQL) and how to solve it.
The Advent of LLMs shows the ability of machines to comprehend natural language. These capabilities have helped engineers to do a lot of amazing things, such as writing code documentation and code reviews, and one of the most common use cases is code generation; GitHub copilot has shown the capability of AI to comprehend engineers’ intention for code generation, such as Python, Javascript, and SQL, though LLM’s comprehension AI could understand what we want to do and generate code accordingly.
Based on the code generation capability of LLMs, many people have started considering using LLMs to solve the long-term hurdle of using natural language to retrieve data from databases, sometimes called “Text-to-SQL.” The idea of “Text-to-SQL” is not new; after the presence of “Retrieval Augmented Generation (RAG)” and the latest LLM models breakthrough, Text-to-SQL has a new opportunity to leverage LLM comprehension with RAG techniques to understand internal data and knowledge.
In the text-to-SQL scenario, users must have precision, security, and stability to trust LLM-generated results. However, it’s not that simple to pursue an executable, accurate, and security-controlled text-to-SQL solution; here, we conclude the four key technical challenges using LLM with RAG to query databases through natural language: context collection, retrieval, SQL generation, and collaboration.
