How I Build
with AI
"I use AI" is the most useless line in a 2026 portfolio. Everyone says it. The interesting question is where you let it touch the work, and where you deliberately don't. Here's my actual line, and why I keep the LLM out of the decisions.
Every product role now lists "AI-augmented" as a requirement, and every candidate now claims it. So the phrase has stopped meaning anything. Saying "I build with AI" in 2026 is like saying "I use a computer" in 2005. True, but completely uninformative.
What's actually worth knowing is the judgment underneath it: which parts of the work I hand to AI, which parts I keep, and (the line most people skip) whether I let an LLM make the decisions the user relies on. My answer to that one is no, and that's a deliberate decision, not a limitation. This is how I actually work.
Two completely different questions
People collapse "AI" into one thing. In practice there are two separate questions, and confusing them is where most of the muddle comes from.
Here I lean on AI heavily. It's a force multiplier on the parts of building that are about coverage and speed.
Here I'm conservative. For a scoring or advice product, a non-deterministic black box is usually the wrong call, and I'll explain why.
A lot of "AI products" in 2026 are really the first thing dressed up as the second, an LLM bolted onto a problem that didn't need one, because "AI-powered" sells. I'd rather be precise: AI changed how I build. It rarely belongs in what I ship.
Where AI genuinely earns its place
As a solo builder, my constraint is never ideas. It's hours. AI gives me the leverage of a small team on the parts of building that are about breadth and momentum. Here's how that plays out across my projects.
Why there's no LLM in the decisions
Both products I've built make this choice explicitly, for the same reasons. TryCareerMatch scores your profile against roles with a rule-based engine, deterministic and explainable, and no LLM ever touches the scoring or ranking. A newer paid report tier can add a Claude-written narrative on top, but it only describes the deterministic scores, never changes them. Portugal Data Intelligence makes the same split: its analysis runs on real statistical models, and an LLM is an optional layer that only writes prose, with the numbers it describes always the deterministic ones.
This isn't anti-AI. It's knowing what the technology is good at. An LLM is a brilliant tool for building a rules engine: researching the rules, drafting them, stress-testing them. It's often the wrong tool for being the rules engine at runtime. Telling those two apart is most of the skill.
How I actually work with it
Using AI well is a skill, and it's not the one people assume. It's not about writing clever prompts. It's about being a good editor and a relentless verifier of work you didn't write yourself.
What this says about how I'd build on a team
For an early-stage team, the useful version of "AI-augmented" isn't someone who pipes everything through an LLM and hopes. It's someone who ships faster and knows when not to reach for it, who can tell the difference between AI as an accelerant and AI as a liability you'll be debugging in production for months.
That's the judgment I've built by actually shipping with these tools: use AI to move fast on research, scaffolding, and iteration; keep the product deterministic and explainable where users need to trust it; and own every decision that defines what the thing actually is. The speed is the easy part now. The discipline is the differentiator.
See it in the work
Both case studies show the line in practice: an AI-accelerated build, a deterministic product.