Reducing Text2SQL latency with parameterized query templates

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If your Text2SQL system takes 25-30 seconds to respond, user engagement drops significantly. For teams scaling beyond pilot projects, this latency gap between a working demo and a production-ready tool is the biggest barrier to adoption. Without caching, every question triggers a Large Language Model (LLM) call to generate SQL, and those calls introduce challenges: unpredictable response times, throttling limits, and token costs that grow linearly with traffic. Parameterized query templates provide an intelligent caching layer that in our production deployment, reduced end-to-end latency by 80% and cut token consumption by over 50%, turning a slow prototype into a responsive production system. In this post, we walk through the architecture behind this approach, covering the implementation details, performance results, and lessons learned from running a Text2SQL system in production. When you move AI applications from pilot to production, you need solutions that scale under real traffic and perform consistently. Traditional caching strategies, storing expensive computations once and serving them many times, don’t translate directly to generative AI. End users rarely phrase the same question the same way, context varies between sessions, and outputs depend on small input variations. Yet the underlying principle (caching) still holds value. Rather than abandoning caching entirely, the key is finding the right abstraction layer where similar requests can share cached results. Solution overview We applied the solution described in the following section to a system where …

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