Move beyond hardcoded strings and build a scalable, version-controlled, and testable prompt management system in Rails to manage your AI prompts like professional software.
Read article →A hardcoded prompt typo cost us $2,400 in 72 hours. Here's the engineering discipline, testing strategies, and open-source tool (Promptly) we built to fix it for good.
Read article →A deep dive into architectural patterns using ActiveJob and Redis to solve LLM latency, moving from an 8-second wait to a sub-50ms cached response.
Read article →Most function calling tutorials show toy examples that break in production. This guide provides the 80% foundation you actually need: a security-hardened, multi-turn agent with complete parameter validation, proper error handling, logging, and timeouts. Copy, customize, and build on it
Read article →An advanced guide to building a robust, automated RAG evaluation framework in Rails, with production-minded code for metrics, parallelization, and CI/CD integration.
Read article →The moment I realized we had a problem was when our QA engineer asked, 'How do we know if someone accidentally changed how the AI behaves?' We had prompts scattered across a dozen files, each slightly different, with no way to test or version them. It hit me: we were making the same mistakes Rails solved 15 years ago with hardcoded strings. AI prompts aren't just text, they're critical business logic that shapes user experience. So I built Promptly to bring Rails conventions to AI development, treating prompts like the first-class citizens they should be. The result? 60% faster AI feature development and actual regression testing for AI behavior. Sometimes the best solutions aren't about new technology; they're about applying proven patterns to new problems.
Read article →RAG doesn’t have to mean heavyweight infrastructure. In this post, I show how I wired up a lean Retrieval-Augmented Generation pipeline inside a Rails app using Nomic for embeddings, PgVector for search, and OpenAI for generation. The result is a flexible system: open-source at the embedding layer, powerful where it counts, and simple enough to extend without vendor lock-in.
Read article →Machine learning integration in Ruby applications encompasses supervised, unsupervised, and reinforcement learning techniques applied to recommendation systems, fraud detection, and natural language processing. Practical applications demonstrate how ML capabilities enhance traditional Ruby development workflows and create intelligent software solutions.
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