Human Evaluation & A/B Testing
Blind, order-randomised human comparison that tells you which model to ship, plus quality and safety-floor scoring of sampled production traffic while your experiment runs.
Scope a Model Comparison Supporting a Live Experiment
Who Does What
A live A/B test runs on the client's infrastructure. eQOURSE owns the blind comparison before production and the human quality signal sampled while the experiment runs.
| Client owns | eQOURSE supplies |
|---|---|
| Own traffic, flags and rollout | Design and run the human judgement layer |
| Run the experiment and compute significance | Score sampled outputs blind, at the agreed cadence |
| Decide ship criteria | Report whether the quality signal supports the decision |
| Hold business metrics | Produce the quality metric those metrics cannot see |
Human Preference Evaluation
Equivalent outputs are compared without labels or formatting tells. Order is randomised and counterbalanced, the rubric is piloted, ties are allowed, reasons are captured and agreement is measured.
Why Existing Metrics Cannot Decide
| Signal | Useful for | Why it cannot decide quality |
|---|---|---|
| Thumbs up / down | Cheap, always-on directional signal | Low, non-random response and engagement bias |
| Outcome metrics | Retention, completion and conversion | Lagging, noisy and confounded |
| Behavioural proxies | Acceptance, regeneration, copy and edit | Useful but still proxies |
| Automated eval scores | Cheap regression measurement | Measures what the rubric or judge encodes |
| Safety floors | Hallucination, toxicity and PII constraints | Passing does not identify the better model |
Supporting a Live Experiment
Stratified sampling, blind scoring on cadence, quality guardrails, safety-floor monitoring, segment breakdowns and offline-versus-online divergence diagnosis. The client runs the experiment and computes significance.
Getting the Statistics Right
Peeking
Repeated checks before the planned endpoint can inflate false positives.
Variance moved
A model change can alter metric variance, so sample planning starts with a pilot.
Non-determinism
Close decisions use multiple generations rather than treating one draw as the whole model.
Representative People, Measured Agreement
Panels are recruited by user profile, language, region and domain across 30+ global languages, including 12+ Indian languages and native-script, romanised and code-mixed use.
Quality Is Not the Only Axis
Preference strength is read beside latency and cost. Any example values in the visual explanation are illustrative, not client results.
How an Engagement Runs
- Scope and decision
- Sample and rubric
- Panel calibration
- Blind comparison
- Analysis
- Report and walkthrough
A first comparison is typically 4–5 weeks; later comparisons 1–2 weeks; live scoring batches 2–4 days. All are scope-dependent estimates.
What You Receive
- Preference report with confidence intervals and slices
- Strength distribution and tie rate
- Reason-code analysis and worked examples
- Agreement statistics and panel composition
- Segment warnings
- Reusable rubric and calibration set
- Live quality scoring where scoped
- Product and ML walkthrough
Where Human Evaluation Goes Wrong
| Failure | Consequence | Control |
|---|---|---|
| Unblinded raters | Expectation becomes preference | Strip labels and formatting tells |
| Fixed order | Position becomes quality | Randomise and counterbalance |
| Forced binary choice | Ties become coin flips | Use graded preference with tie |
| Wrong panel | Wrong population measured | Recruit to a documented user profile |
| Peeking | Noise can ship as a win | Agree check cadence; recommend sequential methods |
| Historical power only | Changed variance leaves the study underpowered | Size from pilot-observed variance |
| Single generation | One draw becomes a model property | Repeat where the call is close |
| Aggregate-only reporting | A harmed segment disappears | Report standard slices |
| Thumbs-up as quality | Engagement is mistaken for quality | Use rubric-anchored judgement |
| Binary winner framing | A costly narrow win reads as a mandate | Always report preference strength |
| No agreement statistics | Signal cannot be separated from noise | Report alpha or kappa first |
What We Do Not Do
We do not run production A/B infrastructure, compute the client's significance, force a winner, substitute an unmatched crowd or reuse client prompts and rubrics.
Related Services
LLM Evaluation AI Bias & Fairness Audit AI Red Teaming ASR & Speech Model Testing Computer Vision Model Testing LLM & RLHF Annotation
Human Evaluation & A/B Testing FAQs
Do you run our A/B test?
No. A live experiment runs on the client's infrastructure. eQOURSE supplies blind human quality and safety-floor scoring of sampled outputs, and separately runs the complete offline comparison study before production.
Why not just use thumbs up/down data?
It is directionally useful at volume, but response is low and non-random and often correlates more with engagement than output quality.
What makes a preference comparison trustworthy?
Blinding, counterbalanced order, a piloted rubric, graded preference with ties, reason codes, a representative panel and measured inter-rater agreement.
What is the peeking problem?
Checking repeatedly before a planned endpoint can inflate false positives. We ask about monitoring cadence and recommend an appropriate statistical design, but do not run the client's statistics.
Our offline evals and our A/B test disagree. Which is right?
Potentially both: offline evaluation measures a rubric, while live behaviour also reflects latency, length, formatting and friction. We investigate the outputs and explain the divergence.
How many comparisons do we need?
It depends on effect size and pilot-observed variance. We size the human study from a pilot and state plainly when the sample cannot support the desired claim.
Can you compare models in Indian languages?
Yes. Coverage includes 12+ Indian languages with native-speaker raters, native-script, romanised and code-mixed registers, within 30+ global languages.
What if the two models are equally good?
We report a tie. The decision can then move to cost and latency instead of forcing a preference that does not exist.
How is this different from your RLHF annotation service?
RLHF annotation creates preference data used to train a model. Human evaluation creates preference evidence used to choose between model candidates.
How is this different from your LLM evaluation service?
LLM evaluation asks how good one model is against a rubric. Human evaluation compares candidates head to head and asks which one should ship.