Six months ago, I got that Slack message. You know the one. It was from our CTO, glowing with the kind of urgency that makes your whole day change.
"Team, we need an AI strategy! Let's get a smart summarizer on the dashboard. What's the fastest we can get an MVP out with GPT-4o-mini?"
And you know what? It was exciting. The mandate was simple: “AI on everything. Now.” As a developer, my mind was already racing. I knew I could hack together a prototype by the end of the day. A few lines of Python, an API call, and presto—a little sprinkle of AI magic.
That message kicked off a gold rush. In the six months that followed, my team and I became a feature factory. We shipped a smart search, a customer service chatbot, a tool to parse user feedback, a generator for marketing fluff… 15 features in total. We were the company heroes, shipping “innovation” at a breakneck pace. Management was ecstatic.
But we weren’t innovating. We were setting a trap for our future selves.
We did what they asked, but the speed created a mountain of invisible tech debt that almost ground our entire development team to a halt. This is the story of the mistakes we made, and the three-step system we’re using to dig our way out.
Part I: How We Buried Ourselves in Tech Debt
This new kind of tech debt is sneaky. It doesn’t look like messy code. It looks clean, simple, and modern. But it was a series of time bombs, each one ticking down thanks to three silent killers.
Mistake #1: The “Shiny New Toy” Syndrome
In the mad rush, we grabbed whatever tool was newest or easiest. For the summarizer, we went with OpenAI because everyone was talking about it. For the chatbot, another team had already played with Anthropic, so we used that. For a simple translation feature, someone found some obscure API that was dirt cheap. We had no plan, no strategy—just a desire for speed.
Individually, the code for each feature looked fine. But when you zoomed out, it was pure chaos. We were wrestling with a handful of different API keys, different SDKs, and different bills to pay. When a user complained that summaries were slow, a whole investigation would kick off just to figure out which of our half-dozen vendors was dropping the ball.
The real pain hit three months in, when one of our main providers decided to double their prices overnight. Swapping it out wasn’t a quick fix. It meant rewriting a core part of every single feature that depended on it.
Mistake #2: The Fragile Prompts and the “Who Do We Blame?” Game
We treated our prompts like they were just another line in a config file. They weren’t. They were fragile, whispered suggestions to a machine we didn’t understand. For our feedback summarizer, we had a prompt that felt like a work of art: Summarize the following user feedback into a single, positive-sounding sentence. It worked like a charm.
Until, one Tuesday, it didn’t.
The API provider had pushed a silent update to their model. With no warning, our “positive-sounding” summaries started sounding… sarcastic. The entire logic of our feature was broken, but not a single line of our code had changed. No linter, no static analysis, nothing could have warned us. It’s what I now call a “semantic dependency,” and it’s a total nightmare to debug.
A customer would complain about a weird, off-brand summary. What was I supposed to do? You can’t stick a breakpoint inside a closed-source LLM. You can’t see what’s going on. My daily routine became a frustrating loop of guesswork:
- Is it the prompt? Okay, let’s try re-wording it for the tenth time…
- Is it the user’s input? Maybe they typed something weird… an edge case?
- Is the API just having a bad day? Time to check their status page again…
This isn’t engineering. It’s modern-day divination. I was on the hook for a feature whose brain was a complete black box.
Mistake #3: Using a Nuke to Kill a Fly
That simple API call was a blank check, and we were writing them like crazy. For one of our internal tools—just classifying support tickets—we were using one of the big, general-purpose models. It worked, sure, but our cloud bill started to look like a phone number.
We eventually realized we were using a super-intelligent nuke to kill a fly. A much smaller, fine-tuned, open-source model—heck, even a handful of if/else statements—would have been ten times faster and a hundred times cheaper. But in the “Feature Factory,” all that mattered was getting it out the door. We ended up with sky-high costs and sluggish performance for a feature that never needed that much firepower.
Part II: Digging Our Way Out
About six months in, things started to fall apart. Pushing a new feature would break an old one. Our costs were all over the place. And honestly, the team was getting burnt out from fighting fires. We had to stop, take a breath, and actually start engineering a solution.
Step 1: We Ran a “Tech Debt Triage”
First things first: we couldn’t fix everything at once. We booked a meeting room for a whole afternoon, put all 15 AI features on a whiteboard, and plotted them on a dead-simple 2×2 grid.
(Seriously, draw this on a whiteboard. It’s a game-changer. I recommend creating a simple 2×2 grid image to insert here)
- X-Axis: How much does this matter to the business? (Low to High)
- Y-Axis: How much of a technical mess is this? (Low to High)
That afternoon brought so much clarity.
- Top-Right (High Impact, High Debt): The customer chatbot, for example. This became our immediate priority.
- Bottom-Right (Low Impact, High Debt): That silly “fun fact” generator on the login screen. We decided to kill it. Deleting code never felt so good.
- The other two quadrants: We decided to leave them alone. If it ain’t broke, don’t fix it.
Step 2: We Built a Centralized “AI Gateway”
Our first big project was to fix the “too many APIs” problem. We built one, simple internal service that all our other apps talk to. This “gateway” is now the only thing in our system that talks to outside AI vendors.
(A simple diagram is perfect here: [Our App] -> [Our AI Gateway] -> [OpenAI, Anthropic, etc.]).
This immediately gave us our power back.
- Control: We can swap providers in one place without any of the other apps knowing.
- Savings: We built a simple caching system right into the gateway.
- Sanity: All our logging, monitoring, and alerts are now in one, manageable place.
Step 3: We Made a “Prompt Library”
To stop the nightmare of fragile prompts living in our code, we pulled them all out. We built a simple library for them—it’s really just a table in a database, with the history tracked in Git—that stores every prompt we use.
Now, our apps just ask the library for the right prompt before making a call. This finally separates the “what to say” from the “how to say it.” Our product managers can now tweak and test prompts to their heart’s content, and they don’t need to file an engineering ticket to do it.
It’s About the Long Game
Look, the pressure to “add AI” to everything isn’t going away. But we have a choice. We can keep slapping features together on top of APIs we don’t control, building a future of products that are expensive and impossible to maintain.
Or we can do the real, sometimes slower, work.
Our new rule is simple: we judge every new feature not just on what it can do, but on its long-term cost of ownership. By tackling our debt and building these control layers, we’ve stopped being order-takers in a feature factory. We’re back to being engineers.
The game isn’t just about using AI. It’s about owning your AI stack. Let’s stop feeding the factory and start building things that last.
So, what’s the worst AI tech debt you’ve stumbled into? I want to hear your war stories in the comments.
