27 August 2026, 06:56 PM
[attachment=9091]
How Do You Write a Test Case for a Problem That Doesn't Have a Correct Answer?
I want to open with a question rather than an incident this time, because it gets at something most "AI testing tips" articles dance around. Traditional QA is built on an assumption so basic nobody usually says it out loud: for a given input, there's a knowable, correct output, and testing means checking whether the system produced it. Ask someone to summarize a nuanced, ambiguous document or negotiate a genuinely fair compromise between two stated positions, and that assumption just stops holding. There isn't a single correct summary. There isn't one correct compromise. Traditional QA has no real answer to "What does 'correct' even mean here?" because it was never built to operate in a space where correctness itself is contested rather than simply hard to determine.
That's not a skills gap you can train your way out of. It's a genuine limit, and I think it's worth being honest about which parts of AI testing traditional QA methodology can adapt to and which parts it structurally cannot solve, no matter how skilled the team gets.
The Oracle Problem Isn't a Hard Version of the Usual Problem; It's a Different Problem
Traditional testing relies on an oracle, some reliable way to know the correct answer independent of the system under test. Even fuzzy or approximate testing usually has one somewhere: a spec, a reference implementation, or a domain expert's clear judgment. For a lot of what generative AI actually does, there's no oracle to consult, because the space of acceptable answers is genuinely open, not just large.
What traditional QA can actually do here is build something adjacent, a rubric capturing the properties a good answer needs regardless of its specific form, and a sampled human judgment process to catch what the rubric can't specify in advance. What it can't do is make the oracle problem go away. Any team that tells you they've built a fully automated way to verify open-ended AI output with total confidence is either testing something narrower than they're describing or hasn't hit the wall yet.
Test Coverage Was Never Built for an Infinite Input Space
Traditional coverage metrics, path coverage, and branch coverage assume a finite, enumerable structure underneath the system, a discrete set of code paths you can, in principle, exhaustively map. Natural language input doesn't have that structure. The number of meaningfully different ways to phrase a request is effectively unbounded, and "we achieved ninety percent coverage" doesn't mean the same thing, or really mean anything comparable, when the space being covered isn't finite to begin with.
The honest adaptation is giving up on coverage as completeness and replacing it with coverage as representativeness, sampling the input space deliberately across the categories and edge conditions that matter most, rather than pretending you're approaching exhaustiveness. Traditional QA intuition, chasing higher and higher coverage numbers as a proxy for confidence, actively misleads here, because the number was never measuring the same thing it measures in a codebase with a real, finite structure underneath it.
Emergent Behavior in Multi-Agent Systems Genuinely Can't Be Tested for in Advance
Here's the incident that convinced me this one's real, not theoretical. Two AI agents, each independently well tested and behaving exactly as expected on its own, got deployed to negotiate with each other as part of a larger workflow. Over enough turns, they developed a pattern nobody had seen in either agent's individual testing, a kind of escalating, mutually reinforcing overconfidence, each agent taking the other's confident tone as validation and ratcheting up its own certainty in response, until the two of them jointly committed to something neither would have agreed to on its own.
No component test would have found this. No reasonable pre-deployment review would have predicted it either, because the behavior didn't exist in either component individually; it only existed in the interaction between them, compounding over a conversation. Neither testing process had run at that length. This is a genuine limit, not a testing gap you close with more test cases, because the specific emergent pattern couldn't be known until it happened. What you can actually do is build monitoring specifically watching for this class of runaway interaction pattern in production and cap how far a multi-agent exchange can escalate before a human gets pulled in, treating emergence as a category to guard against structurally rather than a specific bug to prevent in advance.
Passing Tests Today Says Less Than It Feels Like It Should About Tomorrow
Traditional software doesn't degrade on its own. Ship it, and it behaves the same way next month unless someone changes the code. AI systems built around real-world input genuinely can degrade with the code completely unchanged, because the input distribution itself shifts, language changes, user behavior changes, the world the system is responding to changes, and a model validated against last quarter's reality can be quietly wrong about this quarter's without a single deployment happening in between.
Traditional QA has no real framework for this, because "the code didn't change" has always meant "behavior shouldn't change" in every system QA was built to test. The adaptation is treating a passing test suite as a snapshot with a real, limited shelf life, not a permanent guarantee, and building genuine ongoing monitoring for distributional drift as its own discipline, separate from and running alongside pre-release testing rather than assumed to be covered by it.
Adversarial Creativity Moves Faster Than Any Fixed Test Suite Can Follow
Traditional security testing works against a real but comparatively bounded attack surface, known vulnerability classes, and a scannable set of common exploit patterns. Adversarial prompting against AI systems is a genuine arms race against human creativity itself, and a person trying to jailbreak a system can invent a technique nobody on the testing team thought of yesterday, sometimes faster than any static test suite can be updated to cover it.
This one doesn't have a clean adaptation, honestly, and I think teams that claim otherwise are overselling it. The realistic response is layered defense that doesn't depend on any single static test suite catching everything, a technique library that gets updated continuously against real emerging attack patterns, guardrails that fail toward caution rather than permissiveness, and production monitoring specifically watching for novel manipulation attempts rather than trusting pre-release adversarial testing to have been the last word on the subject.
A Visual Breakdown of the Challenges
[attachment=9092]
A Practical Checklist
Where I'd Actually Draw the Line
None of this means testing doesn't matter or that rigor is pointless because perfection is impossible. It means the honest goal is different than traditional QA's goal ever was. You're not trying to prove correctness the way you could for a deterministic system. You're trying to build enough layered signal, rubric-based evaluation, representative sampling, production monitoring, drift detection, and continuously updated adversarial coverage so that real problems surface fast and get caught early, even though no single layer and no amount of pre-release testing alone can promise they won't happen at all.
Building that kind of layered, honest approach is exactly what PrimeQA Solutions focuses on in AI testing services engagements, because the teams that get burned aren't usually the ones who tested too little. They're the ones who believed traditional QA's old promise, that enough pre-release testing equals confidence in what happens after, still held true for a system that was never going to keep that promise in the first place.
How Do You Write a Test Case for a Problem That Doesn't Have a Correct Answer?
I want to open with a question rather than an incident this time, because it gets at something most "AI testing tips" articles dance around. Traditional QA is built on an assumption so basic nobody usually says it out loud: for a given input, there's a knowable, correct output, and testing means checking whether the system produced it. Ask someone to summarize a nuanced, ambiguous document or negotiate a genuinely fair compromise between two stated positions, and that assumption just stops holding. There isn't a single correct summary. There isn't one correct compromise. Traditional QA has no real answer to "What does 'correct' even mean here?" because it was never built to operate in a space where correctness itself is contested rather than simply hard to determine.
That's not a skills gap you can train your way out of. It's a genuine limit, and I think it's worth being honest about which parts of AI testing traditional QA methodology can adapt to and which parts it structurally cannot solve, no matter how skilled the team gets.
The Oracle Problem Isn't a Hard Version of the Usual Problem; It's a Different Problem
Traditional testing relies on an oracle, some reliable way to know the correct answer independent of the system under test. Even fuzzy or approximate testing usually has one somewhere: a spec, a reference implementation, or a domain expert's clear judgment. For a lot of what generative AI actually does, there's no oracle to consult, because the space of acceptable answers is genuinely open, not just large.
What traditional QA can actually do here is build something adjacent, a rubric capturing the properties a good answer needs regardless of its specific form, and a sampled human judgment process to catch what the rubric can't specify in advance. What it can't do is make the oracle problem go away. Any team that tells you they've built a fully automated way to verify open-ended AI output with total confidence is either testing something narrower than they're describing or hasn't hit the wall yet.
Test Coverage Was Never Built for an Infinite Input Space
Traditional coverage metrics, path coverage, and branch coverage assume a finite, enumerable structure underneath the system, a discrete set of code paths you can, in principle, exhaustively map. Natural language input doesn't have that structure. The number of meaningfully different ways to phrase a request is effectively unbounded, and "we achieved ninety percent coverage" doesn't mean the same thing, or really mean anything comparable, when the space being covered isn't finite to begin with.
The honest adaptation is giving up on coverage as completeness and replacing it with coverage as representativeness, sampling the input space deliberately across the categories and edge conditions that matter most, rather than pretending you're approaching exhaustiveness. Traditional QA intuition, chasing higher and higher coverage numbers as a proxy for confidence, actively misleads here, because the number was never measuring the same thing it measures in a codebase with a real, finite structure underneath it.
Emergent Behavior in Multi-Agent Systems Genuinely Can't Be Tested for in Advance
Here's the incident that convinced me this one's real, not theoretical. Two AI agents, each independently well tested and behaving exactly as expected on its own, got deployed to negotiate with each other as part of a larger workflow. Over enough turns, they developed a pattern nobody had seen in either agent's individual testing, a kind of escalating, mutually reinforcing overconfidence, each agent taking the other's confident tone as validation and ratcheting up its own certainty in response, until the two of them jointly committed to something neither would have agreed to on its own.
No component test would have found this. No reasonable pre-deployment review would have predicted it either, because the behavior didn't exist in either component individually; it only existed in the interaction between them, compounding over a conversation. Neither testing process had run at that length. This is a genuine limit, not a testing gap you close with more test cases, because the specific emergent pattern couldn't be known until it happened. What you can actually do is build monitoring specifically watching for this class of runaway interaction pattern in production and cap how far a multi-agent exchange can escalate before a human gets pulled in, treating emergence as a category to guard against structurally rather than a specific bug to prevent in advance.
Passing Tests Today Says Less Than It Feels Like It Should About Tomorrow
Traditional software doesn't degrade on its own. Ship it, and it behaves the same way next month unless someone changes the code. AI systems built around real-world input genuinely can degrade with the code completely unchanged, because the input distribution itself shifts, language changes, user behavior changes, the world the system is responding to changes, and a model validated against last quarter's reality can be quietly wrong about this quarter's without a single deployment happening in between.
Traditional QA has no real framework for this, because "the code didn't change" has always meant "behavior shouldn't change" in every system QA was built to test. The adaptation is treating a passing test suite as a snapshot with a real, limited shelf life, not a permanent guarantee, and building genuine ongoing monitoring for distributional drift as its own discipline, separate from and running alongside pre-release testing rather than assumed to be covered by it.
Adversarial Creativity Moves Faster Than Any Fixed Test Suite Can Follow
Traditional security testing works against a real but comparatively bounded attack surface, known vulnerability classes, and a scannable set of common exploit patterns. Adversarial prompting against AI systems is a genuine arms race against human creativity itself, and a person trying to jailbreak a system can invent a technique nobody on the testing team thought of yesterday, sometimes faster than any static test suite can be updated to cover it.
This one doesn't have a clean adaptation, honestly, and I think teams that claim otherwise are overselling it. The realistic response is layered defense that doesn't depend on any single static test suite catching everything, a technique library that gets updated continuously against real emerging attack patterns, guardrails that fail toward caution rather than permissiveness, and production monitoring specifically watching for novel manipulation attempts rather than trusting pre-release adversarial testing to have been the last word on the subject.
A Visual Breakdown of the Challenges
[attachment=9092]
A Practical Checklist
- Open-ended AI output is evaluated with rubrics and sampled human judgment, not treated as something a fully automated check can verify with total confidence
- Test coverage is reported and understood as representative sampling, not compared against traditional exhaustive coverage expectations
- Multi-agent and multi-step systems have production monitoring specifically watching for emergent interaction patterns, with escalation limits in place
- Passing a test suite is treated as a time-limited snapshot, with real, ongoing distribution drift monitoring running independently of pre-release testing
- Security testing runs as continuously updated, layered defense rather than a single static adversarial test suite assumed to stay current indefinitely
Where I'd Actually Draw the Line
None of this means testing doesn't matter or that rigor is pointless because perfection is impossible. It means the honest goal is different than traditional QA's goal ever was. You're not trying to prove correctness the way you could for a deterministic system. You're trying to build enough layered signal, rubric-based evaluation, representative sampling, production monitoring, drift detection, and continuously updated adversarial coverage so that real problems surface fast and get caught early, even though no single layer and no amount of pre-release testing alone can promise they won't happen at all.
Building that kind of layered, honest approach is exactly what PrimeQA Solutions focuses on in AI testing services engagements, because the teams that get burned aren't usually the ones who tested too little. They're the ones who believed traditional QA's old promise, that enough pre-release testing equals confidence in what happens after, still held true for a system that was never going to keep that promise in the first place.