Rethinking Testing for Generative AI: Why BDD and Confidence Matter
Generative AI changes the game, not just in what systems can do, but how we build and test them. If you're rolling out an AI-powered agent in Salesforce or CRM environments, the old rules of software testing simply don’t hold up.
The Challenge: Why Traditional Testing Doesn’t Work
Traditional (deterministic) systems are predictable: input goes in, the same output comes out. You write a test, assert a result, done.
Generative AI is different. It uses probability and language models to generate answers, meaning outputs can vary slightly (or a lot), even with the same input. This makes it impossible to test with strict, black-and-white “pass/fail” assertions.
It’s like asking five people to write a summary — all five might be correct, but none will be identical.
The Shift: From Pass/Fail to “How Confident Are We?”
Instead of asking “Is this exactly correct?”, teams need to start asking “Is this answer good enough, and do we trust it?”
This is where confidence-based testing comes in. We measure:
How sure the model is about its answer
Whether it meets a defined quality threshold
If it follows known safety or brand rules (e.g. no PII, no hallucinations)
When the model isn’t confident, it escalates, defers to a human, or responds with guardrails in place.
Behaviour-Driven Development (BDD): Aligning Everyone Early
Enter BDD, a human-readable way to define what good behaviour looks like.
Instead of writing code-first, BDD starts with shared scenarios:
Given a user asks a question we don’t have data for,
When the AI can’t generate a confident response,
Then it politely escalates to a support agent.
This makes AI behaviour explicit and testable — and everyone from product to compliance to dev can agree on it before a single line of code is written.
Heuristics and Probabilities: Testing in the Grey Zone
Because AI is heuristic-based, we test using:
Thresholds: Is the confidence score over 90%?
Evaluators: Does a second model agree this output is safe and useful?
Metrics: Across 100 prompts, does the AI perform reliably 95% of the time?
The goal is to define an acceptable range, not a perfect answer.
Building It: How to Get Started With Your First Agent
Here’s a quick playbook for safely rolling out your first agent using BDD + confidence testing:
| Step | Description |
|---|---|
| 1. Define AI Behaviour Early | Use real-world customer data and historical scenarios to define AI behaviour up front. Ensure test coverage spans language variations, spelling mistakes, and edge cases. |
| 2. Implement Confidence Thresholds | Set minimum standards before responses go live. If confidence is low, trigger escalation paths or “sorry, I’m not sure” fallbacks. |
| 3. Automate Evaluation | Use another AI model or rule-based evaluator to assess quality, tone, or completeness of generated answers. |
| 4. Monitor Post-Launch | Confidence testing doesn’t stop at go-live. Track how often fallbacks occur, which queries the AI struggles with, and tune thresholds over time. |
| 5. Treat QA as Continuous | Think of AI as probabilistic — it evolves. Regularly re-test with fresh inputs and flag drift, regressions, or risky edge cases. |
Final Thought
Generative AI isn't about perfect answers — it's about aligned behaviour, reliable confidence, and clear fail-safes.
By combining BDD with confidence-based testing, you get a foundation that scales with trust. And in today’s CRM landscape, trust is your differentiator.
Let’s talk
Let’s avoid the next AI rollout making headlines for all the wrong reasons.
If you're thinking about testing, trust, or getting to production — connect with me on LinkedIn.
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