Building a Production-Ready Optimised Text-to-SQL AI System Using LLMs, Vector Databases, and Schema Intelligence Artificial Intelligence is changing the way users interact with databases. Instead of writing SQL manually, users now expect to ask questions like: “Show me revenue by city for the last 6 months.” or “Which candidates cleared the technical interview with scores above 80?” and instantly receive the correct SQL query and chart. This is called Text-to-SQL Generation . In this blog, we will learn how to build a production-grade Text-to-SQL AI system using: LLMs (GPT/OpenAI) PostgreSQL pgvector Ruby on Rails Schema metadata Join intelligence Vector search SQL validation Semantic layers We will also cover a real-world ATS (Applicant Tracking System) example with: Jobs Candidates Applications Interview stages Scores Recruiters This article is designed for developers building: AI dashboards AI analytics platforms AI copilots BI systems Interna...
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AI Automation: How I Built an End-to-End AI Agent for Production Fixes a Zero-Touch Incident Response
Orchestrating a Zero-Touch Incident Response with AI Agents Zero-Touch Incident Response: How I Built an End-to-End AI Agent for Production Rollbar Fixes The Problem: As developers, we’ve all been there: a Rollbar alert hits. You stop coding, open the browser, find the stack trace, search your local repo for the file, checkout a branch, fix it, write a Jira ticket, draft an RCA in Confluence , push to GitHub , and then manually paste the PR link into Slack . This process isn't "engineering"—it's administrative toil. I decided to automate the entire lifecycle using the Cursor AI Agent . The Architecture: A Closed-Loop AI Workflow The goal was to create a "Self-Healing" loop where the AI acts as the Project Manager, Developer, and Technical Writer simultaneously. Step 1: Tools: The Integration Foundation (Token Generation) To make this work, you need to grant the Cursor Agent "API keys" for your ecosystem. Here is the exact...