Showing items from Techniques

The Real-World Trade-Offs of Natural Language to SQL Systems

Natural language to SQL (NL2SQL) systems are one of the most compelling applications of language models—but also among the hardest to get right in production. Turning messy human questions into precise, performant SQL queries touches nearly every challenge in AI: ambiguity, domain context, schema evolution, and validation. Here’s a breakdown of the core lessons learned from real-world implementations—and why building a robust NL2SQL stack requires more than just dropping an LLM behind an input box.

Continue Reading

Effective Practices for Measuring and Managing Technical Debt

Technical debt is a concept familiar to many software developers: the idea that suboptimal solutions or shortcuts taken during development can result in challenges down the road. This metaphor likens it to financial debt, where the “interest” is paid over time through the cost of maintaining or updating substandard code. Managing this debt is crucial for maintaining long-term productivity and system health.

Continue Reading