Chaos-proof delivery: shipping AI with TDD + CI

The myth

"AI moves too fast for discipline."

Reality: AI moves too fast **without** discipline.

The next model drop could change everything. If you don't have tests, evals, and safety checks, you'll spend more time debugging than building. Engineering discipline isn't overhead—it's the only way to move fast sustainably.

TDD for AI

You're not unit-testing the model. That's not your job, and it's not possible anyway. You're testing the **system around the model**:

What you test

**Prompt contracts** — given this input, the output matches this schema

**Tool schemas** — arguments are validated, responses are typed

**Parsing and validation** — malformed outputs are caught and handled

**Fallback behavior** — when the model fails, the system degrades gracefully

**Retrieval relevance** — your RAG pipeline returns useful context

**Golden eval sets** — 30–100 scenarios that define "correct behavior"

What you don't test

Whether GPT-4 understands philosophy

Whether the model is "intelligent"

Random sample outputs without criteria

The pipeline

Here's what a production AI pipeline looks like:

Push → Typecheck → Lint → Unit Tests → Golden Evals → Budget Check → PR Preview → Merge → Deploy → Observability

Every step is automated. Every gate is explicit. Every failure blocks the deploy.

The pieces

1. **Typecheck + lint** — catch dumb mistakes immediately

2. **Unit tests** — verify your system logic works

3. **Golden evals (30–100 scenarios)** — verify the AI behavior is acceptable

4. **Budget regression check** — ensure costs haven't spiked

5. **PR preview deploy** — see the change in a real environment

6. **Observability** — traces and alerts for production

Why this works

When the next model drops:

Your tests tell you what broke

Your evals quantify the impact

Your fallbacks keep users happy

Your traces explain what happened

Without discipline, model drops become fire drills. With discipline, they become routine upgrades.

The path forward

I build MVPs with a real delivery pipeline from day one—so you can keep shipping when the next model drop changes everything.

Book a call to discuss your current setup and where the gaps are.