
    AWS and Cisco developed an enterprise-grade natural language to SQL generation solution that enables accurate, low-latency database querying by breaking complex queries into focused steps and using smaller, more cost-effective language models.


<div>
<p>
This article discusses an advanced enterprise-grade approach to natural language to SQL (NL2SQL) generation using large language models (LLMs), focusing on balancing accuracy, latency, and scalability.
</p>
<ul>
<li>Challenges include complex database schemas, diverse query types, and LLM knowledge gaps</li>
<li>Solution involves breaking down NL2SQL generation into focused, sequential steps</li>
<li>Key optimization techniques include:
<ul>
<li>Mapping queries to specific data domains</li>
<li>Resolving identifiers before SQL generation</li>
<li>Abstracting complex data structures</li>
<li>Augmenting data with detailed definitions</li>
</ul>
</li>
<li>Tested solution showed over 95% accuracy and consistency</li>
<li>Enables use of smaller, more cost-effective LLMs</li>
</ul>
<p>
The methodology provides a scalable approach to generating accurate SQL queries from natural language across complex enterprise data environments.
</p>
</div>


Related articles