Dev Nuggets

When I first started thinking about AI-First development, I framed it like this: AI systems interpret intent, generate outputs, and orchestrate workflows. That sounds reasonable. It’s also incomplete. After building a system around these ideas (WORKS Commons), I realized I was attributing too much to the model—and not enough to the system. The Misconception I thought: AI orchestrates the system. What I learned: The system orchestrates. AI contributes. That distinction is not semantic. It clarifies where control actually lives. What Changed In practice, reliable systems don’t depend on AI making decisions about what happens next. Instead: The LLM produces structured signals Those signals are captured and persisted as state The system uses that state to drive workflow execution The intelligence does not sit in the model. It emerges from: structured state controlled workflows feedback loops The Key Shift I stopped thinking in terms of: prompt ...

A Practical View from WORKS Commons Introduction: The Shift to AI-First In today’s tech landscape, "AI-First" is a term that gets thrown around frequently, often generating both excitement and apprehension. For many engineers, there is a lingering concern that an AI-first approach implies a loss of agency—that we are handing the steering wheel to a black box. However, my experience building AI-driven systems suggests otherwise. AI-First development is not about removing control from engineers. It is about shifting from static, hand-coded workflows to systems that interpret intent through AI-generated signals, while maintaining deterministic control over execution. In this model, humans define goals and constraints. The system—not the model—controls workflow execution. LLMs contribute structured outputs that inform decisions within that system. The Concept: What is AI-First Development? To understand AI-First, it helps to distinguish it from "AI-Added" system...

Below is a sample of the serialized interview metadata. This data feeds the “Trainer Agent” (or agentic process). Currently, the Interviewer itself is not an agent—the “intelligence” is orchestrated by WORKS Commons. That distinction is pivotal for the Interviewer → Trainer pipeline. I’ve learned a lot building this. I love it. Model prompt engineering is part discipline, part art. Good practices are essential to reduce hallucinations. A strong prompt must: - Define clear instructions and rules - Specify how the data should be used - Establish boundaries and limitations - Explicitly describe the data schemas involved Anything outside those boundaries should be treated as: “I do not know.” Clarity here matters. A lot. WORKS Commons does not rely on HITL (Human-in-the-Loop) to mitigate hallucinations. When we use generative tools and push back in the editor—adding constraints, refining instructions—we are participating in the generation process. We reclaim some agency. WORKS Commons tak...