5 Steps to Evaluate AI Systems for Production

You cannot ship an AI feature without knowing what goes wrong.

I have worked on AI products in different companies and industries. In energy companies, system downtime costs thousands per minute. In fintech, a bug can lock customer accounts and stop people from paying bills. In startups, launching a broken AI product kills the entire company.

In all these places, "it works in testing" was never enough. The real question was always: is this ready for actual users?

Last month, a team showed me an AI assistant they had built in 3 days. They wanted to launch it in a week. I said: wait. Before you do, let us evaluate whether this actually works with real people.

Here is the process I use. It takes a few weeks but saves months of problems later.

Step 1: Define what success actually is

Most teams skip this. They build something, test it on 10 examples, and launch. Wrong order.

Before you write any code, write down what success looks like. Do not think about the AI. Think about the person using it.

Example: a legal firm has paralegals reviewing tenant inquiries. What is success?

• Success: Paralegal can review AI-generated advice and send it to the client in 5 minutes
• Not success: AI is 95 percent accurate

The accuracy does not matter if it takes 20 minutes to verify. The speed matters because the paralegal is the one responsible for sending it.

Another example: customer support chatbot. What is success?

• Success: Chatbot resolves 80 percent of basic questions without escalating
• Not success: Natural language is perfect

Define success by what matters to the human. Not by metrics.

Step 2: Build a test set from real cases

Get 15 to 20 real examples from actual users. Not fake cases. Real ones that actually happened.

For the legal firm example, I took:

• 10 routine inquiries (straightforward cases the system should handle)
• 3 tricky cases (where the law itself is unclear)
• 2 completely out of scope (cases the AI should not try to answer)

Then I tested the AI on these 20 cases.

Results: 15 out of 20 correct. But the failures tell the real story:

• 3 failures were genuinely ambiguous (the law does not give a clear answer, so the AI confusion makes sense)
• 2 failures were hallucinations (AI made up a legal precedent that does not exist)

This changes everything. 75 percent accuracy is actually fine if humans know they need to review the other 25 percent. But 100 percent confidence on a wrong answer is dangerous.

Step 3: Find the failure modes

What actually happens when your AI gets it wrong?

For legal advice:

• Risk: AI gives bad legal advice, paralegal does not catch it, client gets wrong information
• Fix: Require human review before ANY advice is sent
• Outcome: AI saves 15 minutes, but a human is always responsible

For customer support:

• Risk: AI escalates wrong issues to wrong team, confusion, bad customer experience
• Fix: When escalating, always include the original customer message so the agent can re-read it
• Outcome: Fewer escalations, and when they happen, the agent knows what the customer actually asked

Think through what breaks. Write it down. Do not hope your team catches problems in production.

Step 4: Set confidence thresholds

Your AI does not always know if it is right. That is useful information.

Add confidence scores to every answer. Then route based on confidence:

• High confidence (greater than 85 percent): Ship to users without review
• Medium confidence (70 to 85 percent): Requires human review
• Low confidence (less than 70 percent): Reject and ask for clarification

For the legal AI:

• Routine cases with 90 percent confidence: paralegal can review the draft in 2 minutes
• Tricky cases with 70 percent confidence: paralegal reviews more carefully
• Edge cases with less than 60 percent confidence: Please contact a lawyer directly

This transparency is more important than having perfect accuracy.

Step 5: Monitor in production

Ship to a small number of users first. Watch what actually happens.

For the legal startup:

• Week 1: Handle 10 percent of cases. I reviewed every single output
• Week 2: Handle 30 percent of cases. Found 1 hallucination, fixed the system prompt
• Week 3: Handle 70 percent of cases. System working as expected
• Month 2: Handling 90 percent of routine cases without major issues

Real users do not follow your test cases. They find new edge cases and weird inputs. Monitor, adjust, and iterate.

Why this matters

I watched a chatbot company launch without this process. After 2 weeks in production:

• Over 100 hallucinations were reported by users
• Customers stopped trusting the product
• They had to shut it down for a week to fix it

That week of emergency fixes took 200 engineering hours. The evaluation process would have taken 40 hours upfront.

The question is not "Is my AI smart enough?" The question is "Is my AI responsible enough to ship?"

This framework answers that.