Querying SQL Database Using LLMs — Is It a Good Idea?

Author(s): Sachin Khandewal

Originally published on Towards AI.

Exploring advanced prompting tools to query SQL databases in natural language using LLMs and also highlighting the limitations of these techniques.

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TLDR;

In this blog I will explore different ways to query SQL Databases using Large Language Models; I will use Groq to access the LLMs.I will leverage LLM Agents to build a SQL Agent using an advanced framework — DSPy.While working through the problem, I will also highlight the limitations that are currently faced in this area.

These last 2 years have been absolutely WILD in terms of the advancements in the Language Models space, these advancements have enabled us to pursue some fantastic solutions like Retrieval Augmented Generation and now Agentic solutions as well. But one thing that always remains constant:

Context is King

Without proper context, any LLM cannot solve a domain-specific task, so the problem of building these advanced LLM apps boils down to how good you are at providing context for a particular query.

Nearly all the customer-facing companies are now in a demand to build helpful agents on top of their existing data, or they are in the process of realizing that they need an interactive system on top of their old data. This data can be huge and stored in PostgreSQL, Oracle, MongoDB etc…. Read the full blog for free on Medium.

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Published via Towards AI