The next generation of analytical tools will enable users to ask their data questions in their own words.
The advent of large language models (LLMs) like GPT-3, PaLM, and Anthropic’s Claude has sparked a revolution in natural language AI. These models can understand complex language, reason about concepts, and generate coherent text. This has enabled conversational interfaces for various applications. However, a vast majority of enterprise data resides in structured SQL databases like PostgreSQL, Snowflake, and BigQuery. Seamlessly accessing and analyzing this data through natural conversations remains challenging.
Recent research has proposed techniques to enhance the integration of LLMs with SQL databases, with a focus on cross-domain and cross-compositional generalization. For example, Arora et al. have devised an algorithm to sample a diverse set of few-shot examples covering all SQL clauses and operators to effectively prompt the LLM. Their domain adaptation approach adapts these few-shot examples to the target database via semantic similarity. Further, the least-to-most prompting technique decomposes few-shot examples into sub-questions with intermediate representations to improve compositional generalization.
In this article, we explore the promise and challenges of integrating large language models with SQL databases.
The motivation is simple — imagine analysts or business users conversing with data in plain English without needing to write SQL queries.
This could democratize data access, accelerate analytics, and unlock new possibilities for data-driven automation.
However, effectively achieving this human-like semantic interaction between LLMs and SQL requires overcoming key challenges around query correctness, security, and performance.
We’ll discuss techniques like careful prompting, validation loops, and role-based access control that can pave the path towards production LLM-SQL integration.
Combining the conversational prowess of LLMs with the analytical richness of SQL could transform how humans interact with data. The future where data conversations sound as natural as human conversations may not be far away!