Posts tagged with #ai

May 7, 2026

How CLAUDE.md actually works

Most engineers write CLAUDE.md like a README. They put their stack, a few preferences, maybe a note about testing. The agent reads it and proceeds to write code shaped by its training data rather than their codebase. The file exists. It doesn't do much. The problem is category. A README describes a project. A specification constrains behavior. This post covers three dimensions of getting it right: how to structure the file across a real directory hierarchy, how to write rules the agent actually follows instead of weighs, and how to keep the file current as the codebase evolves.

October 19, 2025

#rails #ruby #ai #patterns

Scaling AI Prompts: A Guide to Building a Prompt Management Engine in Rails

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.

18 October 2025

#rails #ruby #ai #patterns

The Prompt Engineering Manifesto: From $2,400 Mistakes to Production-Grade AI

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.

October 7, 2025

#ai #rails #performance #caching #background-jobs

From Waiting to Working: Building Responsive AI Features in Rails

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.

October 6, 2025

#ai #ruby #agents #llm #patterns

Building a Production-Ready AI Agent Foundation in Ruby

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

October 4, 2025

#ai #rag #rails #testing #evaluation

A Production-Minded Guide to RAG Evaluation in Rails

An advanced guide to building a robust, automated RAG evaluation framework in Rails, with production-minded code for metrics, parallelization, and CI/CD integration.

2025-08-18

#ai #rails #ruby #open-source #prompt-engineering

How I Built Promptly: Solving AI Prompt Management at Scale

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.

2025-07-18

#ai #rails #rag #postgres #ruby

How I Built a RAG System in Rails Using Nomic Embeddings and OpenAI

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.

September 19, 2024

#machine-learning #ai #ruby #intro

What is Machine Learning?

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.

← Back to Articles