DJC Insights
Avoiding Technical Debt in AI Products
Technical Debt is a well-known concept: Quick code now = Painful cleanup later.
But AI Technical Debt is different. It is stealthier, and it accumulates faster.
The Hidden Debt of ML
A Google research paper famously called Machine Learning "The High-Interest Credit Card of Technical Debt." Why?
- Data Dependencies: Code depends on stable logic. AI depends on data. If your input data changes format slightly, the model's accuracy tanks, but the code doesn't crash. It fails silently.
- Glue Code: AI systems are often 5% model code and 95% "glue" code (data piping, formatting). This glue is often messy and brittle.
- Model Drift: The code didn't change, but the world did. The model is now "legacy" just by existing.
Strategies to Pay the Debt
1. Version Everything
Not just code. Version your Data. Version your Prompts. Version your Models. If you can't rollback to "The state of the system last Tuesday," you are in debt.
2. Isolate the AI
Don't sprinkle OpenAI calls throughout your backend logic. Wrap the AI in a service. Treat it like a volatile 3rd party API. If you switch models later, you only change the wrapper, not the whole app.
3. Eval Suites are Unit Tests
You wouldn't ship code without Unit Tests. Don't ship AI without Evals. Have a dataset of 50 "Golden Inputs." Run the prompt against them. Did the score drop? If yes, don't deploy.
Clean Code > Clever AI
A mediocre model in a clean, robust architecture is valuable. A state-of-the-art model in a spaghetti codebase is a liability.