If you're building with AI, you've probably had that moment: your product just did something weird. Maybe it gave a user the wrong answer. Maybe it hallucinated confidently about something that never happened. Maybe it just felt... off.
Your first instinct might be to shrug it off as "well, that's AI for you" and move on. But here's the reality: your users won't shrug it off. And neither will your business when the consequences catch up.
The Cost of Crossing Your Fingers
Most teams treat AI quality and ethics like they treat terms of service, something to figure out later when they have time. They're busy shipping features, iterating fast, and trusting that things will probably work out fine.
But "probably fine" isn't a strategy.
When your AI makes mistakes, you're dealing with trust. A broken button is annoying. An llm that confidently gives wrong information about {available rooms}, misrepresents your brand, or discriminates against certain users? That's a business risk with significant trade-offs.
The companies that treat evals and ethics as afterthoughts are the ones scrambling to explain to customers why their AI failed. They're the ones learning about edge cases from angry users instead of controlled experiments, often missing the mark on reliability and precision.
What Actually Happens When You Wait
I've seen this play out a fair amount of times. A team launches an AI feature without proper evaluation. At first, everything seemed great. Users are engaging. Metrics look good. Then:
- A customer posts a screenshot of your AI saying something wildly inappropriate
- You realize your model performs significantly worse for certain user segments
- A competitor ships something better and you have no data to understand why
- Your team wants to switch models but has no way to know if it will break things
Now you're in reactive mode. You're building evals under pressure, trying to handle llm evaluation and define quality standards while also doing damage control. You're learning expensive lessons that could have been caught early, with a focus on failure cases.
The Unfair Advantage of Getting It Right Early
Here's what changes when you build evals and ethical guidelines from the start:
You ship with confidence instead of anxiety. You are prepared to catch most problems before users do. You have data to back up your decisions when stakeholders ask tough questions. You can iterate quickly because you know what "better" actually means.
More importantly, you build trust. Users notice when AI products are thoughtfully built. They notice when companies care about getting things right, not just getting things shipped. This is crucial for the success of any AI initiative.
Start Small, Start Now
You don't need a PhD in AI safety or a dedicated ethics team to start doing this right. You need to ask better questions before you ship:
- What does "good" actually look like for this feature?
- How will we know if it's working as intended with specific evaluation metrics?
- What are the worst-case scenarios and how do we prevent them?
- Who might this impact differently and why?
Then test those assumptions. Build simple evals. Review real outputs. Talk to users from different backgrounds. Document what you learn. Consider incorporating human feedback, such as thumbs-down indicators, to gauge user satisfaction.
The best time to think about AI quality and ethics was before you started building. The second best time is right now, before your users force you to, using an effective electronically-driven performance evaluation system.
The Bottom Line
Treating evals and ethics as afterthoughts doesn't save time. It just delays the inevitable reckoning and makes it more expensive when it arrives. The companies winning with AI aren't just the ones moving fastest. They're the ones moving thoughtfully, considering their specific workflow and understanding the specific scenario at hand.
Your AI will fail sometimes. That's inevitable. But whether all those failures are caught in controlled environments or left to be discovered by users in the wild? That's within your control.
I write more about evals and building thoughtful AI systems here.
