Llm on Robert Terakedis

Building a Leaner AI Development Workflow for Claude Pro

Mon, 15 Jun 2026 00:00:00 +0000

Earlier this year, I started using BMAD as a structured way to build software with Windsurf at work. The basic idea is sound: write planning docs first (PRD, architecture, epic breakdown), then give the AI one well-scoped story at a time to implement. Instead of one giant "build me this app" prompt that wanders off course, you get repeatable cycles with clear checkpoints.

It worked well enough that I wanted to use it on everything, including some hobby projects at home. Then I ran into the context budget like a brick wall.

Designing an AI Pipeline That Works Everywhere, All the Time

Sun, 14 Jun 2026 00:00:00 +0000

I wrote about the matching logic behind this system in an earlier post. That post covers the five-layer architecture: intake, vector search, LLM scoring, threshold routing, and feedback. What it doesn't cover is the part that took just as much thought: making the whole thing reliable enough that the team can count on it regardless of when or where they're working.

Getting a prototype working on a Friday afternoon is one problem. Getting a system to reliably respond to events around the clock, across a globally distributed team, is a different problem. That's what this post is about.

Why I Stopped Treating LLMs as Magic and Started Treating Them as Components

Sat, 16 May 2026 00:00:00 +0000

I recently got the opportunity to build out an AI-powered feature-matching system. Some of my coworkers had already started mocking the idea - give an LLM a spreadsheet of ideas and features, let it give you back the list of matches. Feed it the customer's idea, get back a match. Easy. I initially made the same assumption a lot of teams make: I treated the LLM as the solution.

It wasn't easy.