Most production AI systems—summarizers, chatbots, recommendation engines, creative tools—produce outputs where no single answer is correct. You can't unit-test these with exact match assertions. Instead, use a layered evaluation approach. Start with rubric-based scoring: define 3-5 quality dimensions (accuracy, helpfulness, tone, completeness) and rate each on a clear scale. Layer in LLM-as-judge evaluation for fast automated feedback using carefully designed scoring prompts. Use pairwise comparison (A vs B ranking) when absolute scores are unreliable—humans and LLMs are often better at comparing two outputs than scoring one in isolation. Sample 10-15% of production outputs for human review to calibrate your automated methods. Combine all four approaches into a tiered stack: automated rubric checks on every output, LLM-as-judge on every change, pairwise comparison for major decisions, and human review on a regular cadence. The goal isn't finding the right answer—it's building confidence that outputs consistently meet a quality bar your users accept.
You build an AI summarizer, a customer support chatbot, a content recommendation engine. You ship it. Then someone asks: "How do you know it's working?"
For a search engine, the answer is straightforward—the user either found what they needed or didn't. For a calculator, it's trivial—the math is right or wrong. But most AI systems don't work that way. When your chatbot generates a response to a frustrated customer, there isn't one correct reply. There are hundreds of reasonable responses and thousands of bad ones, and the line between "good" and "acceptable" is blurry by nature.
This is the testing problem that trips up nearly every AI team we work with. They know how to write unit tests. They know how to validate APIs. But when the system's output is inherently subjective, their testing instincts fail them. A client recently told us they'd been manually spot-checking chatbot responses—reading 20 conversations a week and deciding whether they "felt right." That approach doesn't scale, and it doesn't catch the regressions that matter.
Testing AI systems when there's no right answer requires different methods. Not harder methods—different ones. Here's what actually works in production.
Why Most AI Outputs Don't Have a Single Correct Answer
The uncomfortable reality is that the majority of production AI applications generate outputs where reasonable people would disagree on quality. This isn't a flaw in the system. It's the nature of the tasks we're asking AI to perform.
Consider a summarization system. Give ten skilled human writers the same document and ask each to write a two-paragraph summary. You'll get ten different summaries. All of them could be excellent. Some will emphasize different points. Some will use different structures. None of them are "the answer." Now ask those same ten writers to evaluate each other's summaries, and you'll see disagreement there too. If humans can't agree on what the right answer looks like, exact-match testing is obviously useless.
This extends across AI applications. Chatbot responses need to be helpful, but there are many ways to be helpful. Content recommendations need to be relevant, but relevance depends on context, mood, and preferences that shift constantly. Code generation tools need to produce working code, but working code can be elegant or ugly, efficient or wasteful, secure or vulnerable—and reasonable developers will disagree on which dimension matters most.
The spectrum runs from fully deterministic (classification into fixed categories) to fully subjective (creative writing assistance). Most production systems land somewhere in the middle, and your testing strategy needs to account for that ambiguity instead of pretending it doesn't exist.
Rubric-Based Evaluation: Making Subjectivity Measurable
The foundation of testing subjective AI outputs is converting vague quality judgments into structured, repeatable measurements. Rubrics do this.
A rubric defines the specific dimensions you care about and describes what each score level looks like on each dimension. Instead of asking "is this response good?" you ask separate, concrete questions:
The power of rubrics is that they decompose a subjective overall impression into components that can be scored independently. Two evaluators might disagree on whether a chatbot response is "good," but they'll agree much more often on whether it contains factual errors. The disagreement hasn't disappeared—it's been narrowed to dimensions where it's manageable.
Build rubrics from your actual quality problems. If users complain that your AI is "too wordy," add a conciseness dimension. If support tickets mention wrong information, weight accuracy heavily. The rubric should reflect what your users actually care about, not abstract quality ideals. For guidance on building the evaluation datasets that feed these rubrics, see our guide on building evaluation datasets for business AI.
Start with 3-5 dimensions. More than that makes evaluation slow without proportionally improving insight. You can always add dimensions later when you discover quality issues your current rubric doesn't capture.
LLM-as-Judge: Using AI to Evaluate AI
Rubrics make subjective evaluation structured. LLM-as-judge makes it scalable.
The approach is simple in concept: you send your AI system's output to a separate language model along with the original input, a scoring rubric, and instructions for evaluation. The judge model returns scores on each rubric dimension. This gives you automated evaluation that runs on every output, every change, without human bottlenecks.
Building an effective judge prompt requires more care than most teams expect. A vague instruction like "rate this response quality from 1-5" produces inconsistent, poorly calibrated scores. Effective judge prompts include:
That last point matters. Chain-of-thought judging—where the model explains its assessment before scoring—produces significantly more reliable and consistent evaluations than direct scoring. The explanation forces the model to ground its judgment in specific observations rather than giving a gut reaction.
The critical limitation: LLM-as-judge inherits the biases of the judge model. It tends to prefer verbose responses, may not catch domain-specific errors, and can be fooled by confident-sounding but incorrect outputs. Never treat LLM-as-judge scores as ground truth. Treat them as a fast, scalable approximation that needs regular calibration against human judgment. For a deeper comparison of these approaches, see our analysis of human evaluation versus automated metrics.
Pairwise Comparison: When Absolute Scores Fail
Sometimes scoring outputs on an absolute scale just doesn't work reliably. Evaluators—both human and AI—struggle with questions like "is this a 3 or a 4?" But ask the same evaluator "which of these two responses is better?" and you get consistent, reliable answers.
Pairwise comparison exploits a well-documented cognitive fact: humans are dramatically better at relative judgment than absolute judgment. You can't reliably tell me how heavy a rock is by holding it, but you can almost always tell me which of two rocks is heavier. The same principle applies to evaluating AI outputs.
In practice, pairwise comparison works like this: you take the same input, generate outputs from two different system versions (or configurations, or prompts), and ask evaluators to pick the better one. Over hundreds of comparisons, clear winners emerge. You can convert pairwise results into rankings using algorithms like Elo rating or Bradley-Terry models, giving you a quantitative measure of system quality that's grounded in reliable human preferences.
This method is particularly valuable in three scenarios:
The downside is cost and speed. Each comparison requires showing evaluators two full outputs plus context. It's roughly twice the evaluation effort per data point compared to single-output scoring. Use pairwise comparison strategically for high-stakes decisions, not as your everyday monitoring approach.
Human Evaluation That Doesn't Burn Out Your Team
Human evaluation is the gold standard for subjective AI outputs. It's also the approach most teams implement badly—either reviewing too little to be meaningful or reviewing so much that the team resents it within a month.
The sustainable approach is structured sampling with clear protocols.
Define your sampling rate. For most systems, reviewing 10-15% of outputs weekly gives you enough signal to catch quality shifts without consuming your team. High-stakes applications like medical or legal AI may warrant higher rates. Low-risk content generation might need less. The right rate depends on how much damage a quality regression causes before you'd catch it otherwise.
Use independent evaluators. Each sampled output should be reviewed by at least two people. Measure inter-annotator agreement (Cohen's kappa or simple percentage agreement). If your evaluators agree less than 70% of the time, the problem is your rubric, not your evaluators. Clarify scoring criteria, add examples, and recalibrate until agreement stabilizes.
Rotate evaluators. The same person reviewing outputs every week develops blind spots and fatigue. Rotate evaluation duties across team members on a two-week cycle. This also spreads system understanding across the team—everyone develops intuition for quality patterns.
Track evaluator calibration. Include "anchor" examples with known scores in each evaluation batch. If an evaluator's scores on anchors drift, recalibrate before treating their other scores as reliable. This catches fatigue, mood effects, and gradual standard shifts.
The most important principle: human evaluation exists to calibrate and validate your automated methods, not to replace them. Use humans to establish ground truth, then check that your LLM-as-judge and rubric-based automation produce results that correlate with human judgment. When correlation drops, investigate and recalibrate. For a broader framework on auditing your AI system's quality, see our guide on AI audits for bugs, bias, and performance.
Building a Multi-Layer Evaluation Stack
No single method handles subjective AI testing well on its own. The practical approach is layering methods so each one covers the gaps of the others.
Here's what a production-grade evaluation stack looks like:
Layer 1 — Automated rubric checks (every output). Fast, cheap, catches obvious failures. Flag outputs that score below threshold on any dimension for manual review. This is your first line of defense against quality regressions.
Layer 2 — LLM-as-judge evaluation (every system change). Before any prompt edit, model swap, or configuration change ships, run the full eval suite with LLM-as-judge scoring. Compare against baseline. This catches regressions that individual output checks miss because they lack comparison context. This approach integrates directly into an evals-driven development workflow.
Layer 3 — Pairwise comparison (major decisions). When choosing between model versions, fundamental architecture changes, or significant prompt rewrites, run pairwise human comparisons on 100-200 representative inputs. This gives you high-confidence data for decisions that are expensive to reverse.
Layer 4 — Human evaluation sampling (ongoing calibration). Weekly or biweekly, sample production outputs for structured human review. Use results to recalibrate Layers 1 and 2. Track correlation between automated scores and human scores over time. When correlation drops below your threshold, investigate and adjust.
The cost of this stack is lower than most teams expect. Layers 1 and 2 are automated—the primary cost is LLM API calls for judging, typically under $100 per month for moderate-volume systems. Layer 3 happens infrequently. Layer 4 requires a few hours of human time per week, distributed across the team.
The insight this stack produces is substantially better than any single method. You get real-time monitoring, change-level regression detection, high-confidence comparison data for big decisions, and ongoing calibration that keeps the whole system honest.
Mistakes That Make Subjective AI Testing Useless
Teams that try to test subjective outputs frequently make errors that undermine the entire effort. These are the patterns we see most often.
Collapsing quality into a single number
A chatbot response that scores "3.7 out of 5" tells you almost nothing actionable. Is the accuracy fine but the tone off? Is it helpful but incomplete? A single aggregate score hides the information you need to actually improve the system. Always evaluate and report on individual quality dimensions. Aggregate scores are fine for dashboards, but investigation and improvement require dimensional breakdowns.
Evaluating curated inputs instead of real ones
Test suites built from clean, well-formed examples produce flattering results that don't reflect production reality. Real users submit typos, incomplete questions, ambiguous requests, and queries your system was never designed to handle. If your eval inputs don't include these messy cases, your quality measurements are fiction. Continuously refresh eval cases from production logs.
Not measuring evaluator agreement
If you're doing human evaluation and not measuring how often your evaluators agree, you have no idea whether your scores are signal or noise. Low agreement means your rubric is ambiguous, and all the data you're collecting is unreliable. Measure agreement first, fix the rubric, then trust the scores.
Calibrating once and forgetting
Your AI system changes over time. Your users change. The topics and contexts shift. An LLM-as-judge prompt calibrated three months ago against human evaluation may have drifted significantly. Recalibrate automated scoring against fresh human evaluations monthly. Treat calibration as recurring maintenance, not a one-time setup task.
Optimizing for scores instead of user outcomes
The ultimate measure of your AI system isn't eval scores—it's whether users accomplish what they came to do. Track real-world outcomes (task completion rates, support ticket volumes, user retention) alongside eval scores. When the two diverge, your evaluation methodology needs updating, no matter how sophisticated it is.
What Testing Without Right Answers Actually Requires
Testing AI systems when there's no right answer isn't about finding the answer. It's about building structured confidence that your system consistently produces outputs above a quality bar that matters to your users.
The practical path: start with rubrics that decompose quality into measurable dimensions. Automate evaluation with LLM-as-judge for speed and scale. Use pairwise comparison for high-stakes decisions. Ground everything in human evaluation that runs consistently, not heroically.
Most teams overthink the tooling and underthink the discipline. A simple rubric scored by a well-prompted judge model, calibrated monthly against 50 human evaluations, catches more real quality problems than an elaborate evaluation platform nobody maintains. Start with what you'll actually sustain. The systems that improve are the ones that get tested—especially when there's no right answer to hide behind.
Frequently Asked Questions
Quick answers to common questions about this topic
Use rubric-based evaluation. Define 3-5 measurable quality dimensions—accuracy, helpfulness, relevance, tone, completeness—and score each on a structured scale like 1-5. This converts subjective judgment into repeatable measurements. Combine rubric scoring with LLM-as-judge automation and periodic human review to validate that automated scores match real user satisfaction.