AI isn’t just another technology trend, it’s a major shift in how software behaves, evolves, and fails. For decades, we built our testing strategies on a simple foundation: determinism. Given X, the system returns Y. Every time. Predictability was our friend.
And then GenAI happened.
Today, we’re testing systems whose outputs change with every execution, even when the inputs don’t. Systems that “reason.” Systems that learn the wrong things as easily as they learn the right ones. Systems that fail in ways no UI automation script will reliably catch. These models operate with a degree of freedom and unpredictability that looks less like traditional software and more like… well… a beast that needs taming.
This post outlines how we regain confidence in that unpredictability, based on the framework from my conference session Taming the Beast: Testing the Unpredictable with Confidence and updated with the latest industry practices and the lessons learned from real-world AI engagements across our clients.
The New Testing Reality: Your System Is No Longer Deterministic
In a traditional system, variation is usually a bug. In a GenAI system, variation is a feature.
Given the non-deterministic nature of AI, even a static prompt can produce wildly different outputs, including different structure, tone, phrasing, and occasionally correctness. That’s the heart of the challenge:
If every output is different, how do we know if any of them are correct?
This is the inversion GenAI forces on quality engineering. Instead of verifying exact expected results, we now evaluate:
- Similarity, not equality
- Semantic correctness, not string matching
- Tone, safety, and bias, not just functional flow
Testing has shifted from “assert” to “assess.”
The AI SDLC Has Its Own Failure Modes (And They Start Long Before ‘Testing’)
One of the most important realizations for quality engineers is recognizing that AI systems introduce unique quality risks at every stage, from data acquisition to model monitoring. And these risks are expanding dramatically as organizations adopt:
- Continuous fine-tuning cycles
- Hybrid retrieval + reasoning architectures
- Guardrail frameworks
- Model-as-a-service APIs that change under your feet
The days of “test the UI and move on” are gone.
Modern QA must now account for:
- Problem Definition Risk - Misaligned intentions produce misaligned models (generative errors often begin with conceptual errors.)
- Data Quality Risk - Dirty, biased, or unrepresentative data creates failure patterns that no automated suite will catch.
- Model Risk - Underfitting, overfitting, hallucinations, unexplainable rationale, or poor generalization.
- Integration & Performance Risk - The model may “work” but the system collapses under real-world load or latency constraints.
- Model Drift Risk - The model degrades over time, often quietly.
GenAI testing isn’t just a testing discipline. It’s a cross-lifecycle risk discipline.
Testing GenAI Isn’t About Certainty… It’s About Statistical Confidence
Here are some cornerstone techniques we’ve found helpful in testing non-deterministic systems:
- Output Consistency Evaluation - Ask slightly varied questions and evaluate whether the semantic response stays within acceptable boundaries.
- Diversity & Coverage Testing - Stress the model with biased inputs, edge cases, malformed prompts, slang, sentiment variations, and noisy data.
- Deterministic Assertions for Evaluatable Cases - For scenarios where “gold answers” do exist, compare against known good artifacts or human-approved baselines.
- Fuzz Testing for Resilience - Prompt chaos engineering: introduce typos, incomplete questions, contradictions, nonsense input.
- Multi-Model Cross-Judgment - To reduce model bias and prevent false positives, multiple LLMs evaluate the same output. For example:
- One model evaluates correctness
- A second checks tone
- A third checks safety
LLM-as-a-Judge: Your New Best Friend (If Used Properly)
While human-in-the loop evaluation can increase our confidence in non-deterministic output, it’s simply not scalable. To address this concern, we need to incorporate checking non-deterministic output in our existing automated test suites. A technique we’ve been employing more and more is using LLMs to evaluate GenAI outputs. This is becoming one of the most practical breakthroughs in AI quality engineering.
Modern testing harnesses increasingly rely on LLM evaluators because:
- They scale
- They understand natural language
- They evaluate nuance that is impossible to encode with traditional rules
- They correlate extremely well with human judgment when properly tuned
However, we’ve learned a few essential lessons:
- Never use the same foundation model that powers the app to evaluate its own outputs as it will agree with itself far too often.
- Use structured scoring scales (5 or 10 points) and keep them simple.
- Provide explicit grading rubrics in the prompt.
- Include positive AND negative examples of acceptable outputs.
- Log evaluator outputs, reasons, and deltas across versions to track regression.
- Calibrate thresholds with real-world usage distributions.
- For safety tests, prefer ensemble evaluators (e.g. three models judging independently.)
This combination delivers a repeatable, scalable, cheaper-than-human way to judge AI systems at industrial scale.
Real-World Examples That Expose AI’s Testing Weak Spots
Let’s take a look at a couple of examples of the challenges we face in testing AI-enabled systems.
AI Customer Support Bots
We still see the same issues every day:
- Emotional mismatch (“I’m furious” → “Thanks for your feedback!”)
- Overconfident hallucinations
- Inconsistent resolution guidance
- Safety gaps under slang, profanity, or sarcasm
With today’s multimodal agents, the scope expands to include:
- Voice tone detection
- Emotion-aware reasoning
- Multi-turn memory consistency
- Compliance-bound responses
Testing must now validate both semantic correctness and interaction quality.
AI Resume Screeners
Bias testing has evolved radically. Today’s evaluation includes:
- Counterfactual testing across dozens of protected attributes
- Embedding-space clustering to identify potential discriminatory patterns
- Measuring feature sensitivity (e.g. how much does “university” influence rankings?)
- Detecting proxy attributes the model may have learned implicitly
This is not optional. It’s essential.
Skills Modern Test Engineers Need (And Why They Are Changing Faster Than Ever)
Foundational testing skills form a solid foundation for testing AI enabled systems, but Quality Engineers now need to add the following:
- AI literacy - A baseline understanding of embeddings, tokenization, inference, hallucination patterns, and drift.
- Model evaluation skills - Precision, recall, F1, BLEU, ROUGE, BERTScore, cosine similarity—these are now table stakes.
- Prompt engineering discipline - Knowing how to ask becomes as important as knowing what to test.
- Testing-as-risk-management mindset - We no longer test for certainty. We test to reduce uncertainty.
- Cross-functional fluency - AI testing requires collaboration with:
- Data scientists
- ML engineers
- Prompt designers
- DevOps and MLOps teams
QE is now an orchestration role as much as an execution role.
So… Can We Really Test the Unpredictable?
Absolutely. But not with yesterday’s tools, scripts, or expectations.
The goal isn’t perfect control, rather it’s stable confidence in a system that is, by design, variable.
To tame the beast:
- Evaluate patterns, not instances
- Test distributions, not strings
- Build risk-based guardrails instead of rigid checklists
- Use LLMs to evaluate LLMs, but with the right separation and oversight
- Combine automated evaluators with human-curated gold sets
- Continuously test, monitor, and recalibrate
AI systems won’t get simpler. But our testing approaches can become smarter, faster, and more aligned with how these systems truly work.
And that’s the key: AI-enabled systems don’t break like traditional software, so we shouldn’t test them like traditional software. Our job is not to force determinism onto a non-deterministic system. Our job is to build confidence in its behavior under uncertainty.
That’s how we tame the beast.
Ready to Bring Confidence and Control to Your AI Strategy?
AI-enabled systems demand a new level of rigor, discipline, and observability—far beyond what traditional QE practices were designed to handle. Whether you’re building GenAI features, integrating LLMs into existing workflows, or modernizing your quality engineering function, the risks are real and so are the opportunities for competitive advantage when quality is done right.
At Forte Group, we help organizations:
Test AI-Enabled and AI-Augmented Systems
- Evaluate non-deterministic GenAI outputs with advanced automated harnesses
- Build bias, safety, and factuality testing into your pipelines
- Validate LLM-integrated workflows, RAG systems, agents, and multimodal experiences
- Implement human + AI hybrid evaluation strategies for high-risk use cases
Implement AI-Augmented Quality Engineering Practices
- Integrate LLM-as-a-Judge frameworks into your CI/CD
- Modernize test design, test data generation, and exploratory testing using GenAI
- Equip your QE team with the AI literacy and tooling required for next-generation testing
- Build scalable, risk-based quality strategies across the AI SDLC
If you're looking to de-risk AI adoption, raise the maturity of your QE function, or embed AI into your testing practice with confidence, we can help.
Let’s talk about how to tame the beast in your environment. Reach out to me directly, or visit fortegrp.com to learn more about our AI Quality Engineering offerings.
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