NL2SQL Part 1.

💡Natural Language to SQL (NL2SQL) in the LLM Era

Data has become one of the most valuable resources of our time. Companies in finance, healthcare, logistics, retail, and many other fields collect enormous amounts of information every day. Much of this information is stored in relational databases, which are typically accessed using SQL.

While SQL provides the raw outputs of a query, the critical step lies in interpreting these results. Developing intuition from retrieved data is essential for identifying meaningful patterns, uncovering relationships, and supporting evidence-based decision-making.

This is where Natural Language to SQL (NL2SQL) becomes valuable. Instead of writing SQL code, users can simply ask a question in plain language, such as:

“What are the top five products sold in Asia this year?”

and automatically receive the result—without having to construct a query like:

SELECT product, SUM(sales)
FROM transactions
WHERE region = 'Asia'
  AND date >= '2024-01-01'
GROUP BY product
ORDER BY SUM(sales) DESC
LIMIT 5;

The motivation is clear: make structured data accessible to everyone. In practice, this means:

• Reducing dependency on technical expertise.
• Enabling deeper understanding of data.
• Supporting more efficient research and analysis.

However, achieving this vision introduces significant technical challenges.

The Challenges of NL2SQL

Developing systems that can reliably translate natural language into SQL queries is a long-standing research challenge that sits at the intersection of natural language processing, information retrieval, and database management. The difficulty lies not only in the inherent ambiguity and variability of human language, but also in the structural complexity and heterogeneity of real-world databases. While the vision of democratizing access to data through natural language interfaces is compelling, realizing it in practice requires addressing several deep challenges—ranging from robust language understanding and schema alignment, to handling noisy data, adapting to diverse SQL dialects, and ensuring secure and efficient deployment.

Beyond these technical hurdles, organizational culture plays a decisive role. In many enterprises, data access is still mediated by technical gatekeepers, and decision-making cultures are shaped by long-standing workflows and hierarchies. For NL2SQL systems to be truly effective, companies must embrace a culture of data accessibility—empowering non-technical users, fostering trust in automated systems, and promoting responsible use of sensitive information. Without this cultural shift, even the most advanced systems risk being underutilized or confined to experimental settings.

The next sections will explore the key challenges that stand in the way of realizing NL2SQL at scale.