When building a Natural Language Query (NLQ) interface for a complex, evolving data catalog, the choice of architecture can make or break your solution. This post explores the architectural decisions faced when implementing NLQ for a system with dynamic schemas, frequent changes, and complex query requirements.

The Challenge: Dynamic Data Systems

Our data architecture presented several unique challenges:

Atomicity : Data organized by resource types, with many type-specific fields

Dynamic Evolution : Rapid schema changes (50 → 140 tables in one year)

Complex Queries : Support for tags, date comparisons, and advanced operators (not just equality but > , < , etc.)

High Specificity : Domain-specific query syntax requirements

The key question: How do we build an NLQ system that adapts to these constant changes without requiring retraining or manual updates?

Solution 1: Fine-Tuned Static Models

The traditional approach involves:

Base Model : A foundation model trained on general query syntax

Fine-Tuning…