AI is amazing on day one.
By day ten, reality shows up. The model "forgets" your conventions. It starts changing unrelated files. It breaks tests it didn't touch. Your carefully crafted codebase gradually degrades into what teams politely call "drift" and privately call "slop."
You know the pattern: Week one feels like magic. Week four feels like babysitting. By week eight, you're wondering if AI is actually making things worse.
Here's what most teams get wrong: they think they have an "AI quality problem."
They don't.
They have a reliability system problem.
AI isn't like a junior developer who learns from corrections. It's stateless. Every interaction starts fresh. Without a system—consistent inputs, persistent rules, verification loops, and reviewable artifacts—quality degrades with every iteration.
The good news? Reliability is fixable. You don't need better prompts or smarter models. You need a checklist that keeps work repeatable: same inputs, same rules, same verification, same artifacts.
This is that checklist.
Checklist:
Why this matters: AI output becomes unstable when the model has to invent missing details. Every gap creates variability. Your goal is simple: reduce ambiguity, not write a long prompt.
Practical tip: A good brief is often just: goal + constraints + acceptance criteria + test expectations.
Checklist:
Why this matters: If rules aren't persistent, you'll re-teach the model every time. That's where drift comes from—each run becomes a new negotiation.
Andrej Karpathy nailed it: "Context engineering" beats "prompt engineering." Industrial-strength AI apps succeed through the delicate art of filling the context window with just the right information—task descriptions, examples, relevant data, tools, state, and history. Too little context and performance suffers. Too much and costs explode while quality drops.
Checklist:
Why this matters: A plan is your first anti-drift mechanism. It makes work sequential and reviewable. Without it, agents improvise.
Checklist:
Why this matters: Large diffs don't just slow reviews—they hide mistakes. Smaller diffs make reliability visible.
Checklist:
Why this matters: Verification is the reliability engine. Without it, output quality depends on luck and reviewer attention.
Practical tip: Don't chase global perfection. Enforce checks on touched files/modules first. That's where reliability starts.
As Itamar Friedman pointed out, the percentage of time spent debugging has gone up massively with AI coding. Karl Weinmeister added: "You still need code quality checks in the era of AI. Different prompts, context, and models lead to different outcomes."
Checklist:
Why this matters: A single model can be confidently wrong. A second pass catches blind spots and stabilizes output quality across runs.
Checklist:
Why this matters: Artifacts make output reviewable and repeatable. They also make "why did this happen?" answerable.
Anthropic's context engineering guide emphasizes this: to get the most out of AI agents, you need more than prompts—you need persistent, structured context that agents can reliably reference across iterations.
This is where most AI setups break down. The agent makes a change, review asks for tweaks, the agent "fixes it," and suddenly unrelated code changes appear.
Checklist:
Why this matters: Iteration is where drift is born. Tight context + repeated checks keep output stable.
Warning signs:
What to do: When you see these, don't just correct the PR—update your rules or workflow so it doesn't repeat.
You don't need fancy dashboards. Track a few simple things:
Metrics to watch:
Why this matters: Reliability is about trends. If review rounds drop and CI stabilizes, your system is working.
AI output becomes stable when you stop treating every run like a conversation and start treating it like a pipeline.
Reliable teams don't rely on "better prompts." They rely on:
That's how you keep AI output stable across iterations—without slowing developers down.