Pairwise comparison of two app versions

Absolute scores from an LLM judge tend to bunch up, say everything gets a seven or eight, and they drift when the judge model changes. Two versions can look tied on averages while users clearly prefer one. Asking 'which of these two answers is better for this input' is an easier, more stable judgement for both models and humans. In LangSmith I run both versions as experiments on the same pinned dataset, then pass both experiment names to evaluate() with a pairwise evaluator. I still keep absolute code evaluators for things like schema validity and latency, because preference cannot tell you that a response broke a contract. So pairwise answers 'which is better' and code checks answer 'is anything broken'.

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There are four layers. Versioning: the baseline prompt sits on its production tag in the Prompt Hub, the candidate is a new commit, and the dataset is pinned to one version. Execution: evaluate() runs once per app version against that dataset version, with repetitions, bounded concurrency and metadata for prompt commit, model and app version. Scoring: code evaluators on each experiment handle hard checks, then a pairwise LLM judge compares the two experiments with randomized order, and a pairwise annotation queue collects blind human preferences. Decision and feedback: a ship gate combines judge, human and code results, the production tag moves to B, and online evaluators on sampled production runs feed failures back into the dataset for the next comparison.

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Preference and correctness measure different things. A judge or reviewer may prefer a richer, friendlier answer that breaks the output schema, ignores a refusal rule, or costs more tokens and time. Those are hard contracts with downstream systems, users and budgets, so they act as vetoes, not inputs to a weighted score. If I let preference outweigh them, a pleasant answer that crashes a parser would ship. Keeping them separate also makes the gate easy to explain: first pass every hard check, then win on quality. When a code check fails, the fix is a new candidate commit and a fresh comparison, not an override at the gate.

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I do not ship B. The gate requires both humans and the judge to prefer the candidate, and humans are the ground truth the judge is supposed to approximate. Next I investigate the disagreement: which examples split, whether they cluster in a slice, and whether the judge is rewarding something humans dislike, such as verbosity or confident tone. That usually leads to two pieces of work: improving the judge prompt with those cases as few-shot examples, and improving the candidate in a new commit. Then I rerun the comparison on the pinned dataset. The disagreement itself is a valuable signal that the judge is not yet ready to gate alone.

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First, assess severity from the alert and the dashboards: which metric moved, on which filtered runs, and since when. If it is a hard-rule failure like schema breaks or unsafe answers, I move the production tag back to the baseline commit, which is the fast, low-risk rollback this design gives us. Then I inspect the failing traces, find the pattern the offline dataset missed, and add representative examples to the dataset, creating a new version. The next candidate is compared against the restored baseline on that new version. The post-incident question is why the offline comparison did not catch it, usually a coverage gap.

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First, randomize the order of the two outputs so position bias averages out, and ideally check that verdicts hold when the order is swapped. Second, run repetitions and look at examples where the judge flips; those are low-confidence. Third, calibrate against humans: I put the two experiments into a pairwise annotation queue, have domain reviewers choose blind, and compute agreement between the judge and reviewers. If agreement is weak, I refine the judge prompt with a clearer rubric or few-shot examples and rerun. I also avoid relying on a judge from the same model family as the candidate without that human check. Only once agreement is acceptable do I let the judge cover the full dataset.

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I create a pairwise annotation queue from exactly the two experiments, so each item shows one input with both outputs side by side. Reviewers should not see which version produced which output. I write a short rubric covering correctness, helpfulness and tone, and set the reviewer count per item above one for subjective tasks so I can measure inter-reviewer agreement. Reservations stop two people working the same item at once. To save effort I do not queue everything; I prioritise examples where the judge was split, tied or disagreed across repetitions, plus a random sample to estimate overall agreement honestly. The human preferences then feed the ship gate alongside the judge's win rate.

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The baseline must be exactly what users get today, otherwise the comparison answers the wrong question. The repository may already contain half-merged edits, and a tag in the Prompt Hub points to the specific commit serving production. Resolving the baseline through that tag, and recording the resolved commit in experiment metadata, makes the run reproducible and auditable. It also means promotion is a simple, reversible action: move the production tag from the baseline commit to the candidate commit. If something goes wrong later, moving the tag back restores the known-good version without a redeploy of prompt text.

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Prevention comes first: both evaluate() calls reference the same pinned dataset version rather than the latest state, so edits by teammates create a new version that neither run sees. As a check, I record the dataset version in each experiment's metadata and have the gate script refuse to compare experiments whose versions differ or whose example sets do not match. If a mismatch slips through, I do not try to compare the overlapping subset and call it done; I rerun both versions on one chosen dataset version. Partial overlap silently changes the mix of easy and hard examples, which can flip a win rate.

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LLM apps and LLM judges are both non-deterministic, so a single run can produce a lucky or unlucky output and a lucky or unlucky verdict. Repetitions let me see whether a preference is stable. In the comparison view I look for examples where the judge picked A in some repetitions and B in others. Those are not noise to average away; they usually mark genuinely close calls, ambiguous inputs or a weak rubric. I send them to the pairwise annotation queue with priority, consider tightening the judge criteria, and treat stable wins as much stronger evidence than split ones when deciding the gate.

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At minimum: the resolved prompt commit, the model name and parameters, the app version or git SHA, and the dataset version. I often add the judge prompt version and the repetition count. This metadata turns an experiment from a loose pile of scores into a reproducible record. It lets the gate verify both sides used the same dataset version, lets reviewers filter and group experiments in the UI, and lets anyone months later explain why version B shipped. Without it, you cannot tell whether a difference came from the prompt change, a silent model upgrade or a different set of examples.

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I give the judge the input, both outputs labelled neutrally as A and B, and the reference output when the dataset has one. The criteria are explicit and ordered, for example factual correctness against the reference first, then instruction following, then clarity, with a note that length alone is not a virtue. I ask for a short justification before the verdict, allow a tie when the answers are equivalent, and constrain the output to a fixed format the evaluator can parse. I add a few labelled examples from human decisions as few-shot guidance, and I version the judge prompt so its changes are tracked like any other prompt.

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I work from formulas rather than guesses. App calls equal examples times repetitions times two versions. Judge calls equal examples times repetitions, doubled if I run both orderings instead of a single random one. Human effort equals the number of queued items times reviewers per item. Token cost comes from measured average tokens per call in previous traces, and runtime is roughly total calls divided by effective concurrency, times average latency. Plugging in real numbers from my own traces tells me whether to sample the dataset for quick iterations and reserve the full dataset and full repetitions for the final gate run.

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Datasets built from production traces may contain personal or confidential data, and that data then flows to judge models and human reviewers. I scrub or mask sensitive fields before examples enter the dataset, and mask inputs and outputs in tracing where needed. Access to the project, datasets and annotation queues is restricted to people who need it, and reviewers see only what they need to judge. I am careful about which judge model provider receives the data, given contracts and data residency, which matters for Indian users under the DPDP Act. I also remember that online evaluation extends retention of evaluated traces, so sampling filters should avoid sensitive traffic where possible.

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Three main risks. Overfitting: if every candidate is tuned against the same growing set of past failures, you can win on the dataset while losing on new traffic, so I keep a held-out slice that is not used during iteration. Skew: failures over-represent rare cases, so I tag examples by source and slice and report win rates per slice instead of one blended number. Privacy: production traces may contain personal data that must be scrubbed before becoming permanent examples. Also, each addition creates a new dataset version, so the next comparison reruns both versions on it rather than mixing old and new results.

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Common ones are length or verbosity bias, preferring the longer answer; style bias, rewarding confident or polished tone over correctness; and self-preference, favouring outputs that resemble its own model family. I counter them in the rubric by stating that correctness against the reference comes first and that length is not a criterion, by giving the judge the reference output where available, and by choosing a judge model independent of the candidate when possible. Few-shot examples drawn from human verdicts, especially ones where the shorter answer won, help. Finally I check agreement with human labels per slice to see whether a bias remains.

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I write the gate criteria before running anything, so the result cannot be rationalized afterwards. The criteria cover: no regression on any code evaluator; the judge prefers B over A overall and on each critical slice; human reviewers in the pairwise queue also prefer B; and judge–human agreement meets the threshold the team agreed for that dataset. I do not use a universal number; teams pick thresholds from their own risk tolerance and past agreement data. I also define how ties and split examples are treated. Writing this down in the gate script makes the decision repeatable across releases.

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An online A/B test exposes real users to a possibly worse version and needs enough traffic and time to reach a conclusion, while offline pairwise evaluation catches many regressions before anyone sees them. Offline runs are also controlled: identical inputs on both sides, repetitions and side-by-side diffs, which production traffic cannot give you. User signals in production are often sparse and noisy. The two are complementary. This workflow uses offline pairwise comparison as the gate, then online evaluators on sampled production runs to catch what the dataset did not cover, feeding those cases back offline.

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An error is a result, not something to drop silently. If B fails on an example where A succeeded, that is a regression and the code evaluators or the gate script should count it. If failures are infrastructure noise, such as timeouts or rate-limit errors, the experiments become uneven and the pairwise comparison loses examples, which can bias the win rate. I look at the error traces, fix the cause, often by lowering concurrency or adding retries, and rerun so both experiments cover the full pinned dataset. The gate should refuse to pass if coverage on either side is incomplete.

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I make it a shared pipeline instead of a manual ritual. Each team owns a versioned dataset per feature and a standard gate script with the same steps. To control cost, quick iteration runs use a sampled subset with fewer repetitions, and the full dataset with full repetitions is reserved for the gate run. Judge prompts are shared and versioned, with agreement checked periodically against human labels. Human review is the true bottleneck, so queues get reviewer counts sized to the risk of the change and only contested or high-risk examples go to humans when the judge has proven reliable on that dataset.

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Concurrency is bounded by the slowest shared limit: the model provider's rate limits for the app, the rate limits for the judge model, and any downstream tools the app calls. Too high and you get throttling errors, retries and failed examples that make the two experiments uneven. Too low and the comparison takes so long that people skip it. I start conservatively, watch error rates and latency in the traces, and raise it until throttling appears. I also keep both experiments on similar settings, because heavy throttling on one side can distort latency metrics that the code evaluators are checking.

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Ties are information, not failures. A high tie rate usually means the change is small, or the rubric cannot separate the answers. I report ties separately rather than splitting them half and half into wins, because merging them hides whether the candidate truly wins or simply rarely loses. For the gate, the question is usually whether B wins more than it loses on the examples where a preference exists, and whether the slices that matter most show any losses. If ties dominate and code evaluators show B is cheaper or faster, that can still justify shipping, but the decision should be stated in those terms.

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Randomizing once per judgement is cheaper and, across many examples, removes systematic position bias from the aggregate. Running both orders doubles judge cost but gives a per-example consistency check: if the verdict flips when positions swap, the judge is not really preferring either answer and I treat it as a tie or send it to humans. For quick iteration I randomize once. For the final gate, or when early runs show strong position effects, I run both orders on at least the contested examples. Either way, the order used should be recorded so position effects can be audited.

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Caching helps when I rerun only part of the pipeline, for example iterating on evaluators or the judge prompt while the app outputs stay fixed, so I do not pay again for identical model calls. It misleads when the thing I am trying to measure is the model's variability. If repetitions hit a cache, they return the same output and the repetitions stop smoothing non-determinism. It can also hide a model or prompt change if cache keys do not capture it. So for the gate runs of both versions, I make sure repetitions produce fresh generations and that the experiment metadata reflects the actual configuration.

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It is a trade-off between detection speed and cost. Each evaluated run costs a judge call, and running an online evaluator upgrades that trace to extended retention, which has storage and privacy implications. Right after a release I sample more heavily because that is when regressions are most likely, then reduce the rate once metrics stabilize. I use filters to target risky traffic, such as specific features, error-adjacent runs or negative user feedback, rather than sampling everything uniformly. Cheap code checks can run at a higher rate than LLM judges. Alerts are set on the evaluator metrics so nobody has to watch dashboards continuously.

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Reservations temporarily assign an item to a reviewer who opens it, so two people do not unknowingly work on the same comparison at the same moment, which wastes effort and skews the reviewer counts. The reviewer count per item says how many independent judgements each pair needs before it is complete. If a reviewer opens an item and walks away, the reservation lapses and the item returns to the pool for someone else. Operationally I watch queue progress before the gate date, because a gate that waits on unfinished human review should be visible early, not discovered the morning of the release.

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I would script it. The CI job resolves the baseline from the production tag and the candidate from the new commit, runs evaluate() for each against the pinned dataset version with fixed repetition and concurrency settings, attaches the code evaluators, then runs the pairwise evaluator on the two experiment names. A gate step reads the results and fails the build on any code evaluator regression. Hard assertions can also live in pytest files with the LangSmith pytest integration, which records them as experiments. Human review cannot finish inside a CI job, so the pipeline opens the pairwise queue and the final promotion waits for it.

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Final answers are not enough, because two agents can reach the same answer by very different paths, one of them expensive or unsafe. I add trajectory evaluation, for example a trajectory LLM-as-judge from agentevals, to check whether the tool calls and their order were sensible, and code evaluators for things like forbidden tools, step counts and cost. The pairwise judge still compares final outcomes, but I may give it a summary of each trajectory too. Repetitions matter even more since agent paths are highly variable, and reviewers in the pairwise queue should be able to see the trace, not only the final message.

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The workflow is the same, but a few things need extra care. Metadata must record the exact model identifier and parameters on both sides. Code evaluators for latency and token cost become more important because model swaps often move those more than prompt edits do. Output formatting can shift subtly, so schema checks must be strict. If the judge model comes from the same family as the new candidate, I watch for self-preference and validate against human labels. Rate limits may differ between models, so bounded concurrency settings might need adjusting so one side is not throttled into different behaviour.

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It should reflect the traffic and risks that matter. I combine curated cases written by domain experts, production traces that show real user behaviour and past failures, and synthetic examples to fill gaps such as rare languages or edge inputs. Each example has inputs and, where possible, a reference output the judge can use. I tag examples by slice, for example feature, language or difficulty, so results can be broken down. Size is a balance between statistical confidence and cost per run. And the dataset is versioned, so every comparison names exactly which examples it used.

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Commits are immutable snapshots, so a candidate referenced by commit is exactly the text that was evaluated, with no chance of drifting while the comparison runs. Tags are movable pointers, so the application can load the prompt by its production tag and promotion becomes moving that tag from the old commit to the winning one. This separates evaluation from deployment: evaluation always talks about specific commits, deployment talks about which commit the tag points to. Rollback is the same operation in reverse. Recording resolved commits in experiment metadata keeps the link between what was tested and what shipped.

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Single-turn examples miss problems that build up across a conversation, like losing context or contradicting earlier answers. Offline, I build examples that contain the conversation history as input and ask both versions to produce the next turn, so the judge compares responses given identical context. For full-conversation behaviour I simulate several turns and judge the transcript. In production, threads group multi-turn conversations, and online evaluators can run at thread level to catch issues that only show up over a whole session. Failing threads are good sources for new multi-turn examples in the next dataset version.

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There are several routes. In Python, the @traceable decorator traces your own functions and wrappers such as wrap_openai trace model calls, with tracing switched on through LANGSMITH_TRACING. There are integrations for frameworks like the Vercel AI SDK. If the team already uses OpenTelemetry, LangSmith accepts traces natively through its OTLP endpoint, and a collector can fan out the same spans to LangSmith and the existing observability backend. For the offline comparison itself, evaluate() just needs a callable for each version, so the framework does not matter; tracing mainly helps debugging and online evaluation.

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1. AFewer reviewers than the maximum allowed
2. BA tie rate above zero
3. CAny regression on a code evaluator such as schema validity, refusal rules, cost or latencyCorrect answer
4. DThe judge writing a long justification

Why: Step 3 states that a regression on hard code checks blocks the release regardless of preference.

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1. ABy a new commit created for the test
2. BBy the most recent Playground draft
3. CBy its production tag in the Prompt HubCorrect answer
4. DBy copying the latest text from the repository

Why: The baseline stays on its production tag, which points at the commit serving users today.

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1. AThe OTLP collector logs
2. BThe webhook payload only
3. CThe experiment comparison viewCorrect answer
4. DThe Prompt Hub commit history

Why: Step 5 uses the experiment comparison view for these results.

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1. AB is cheaper than A
2. BJudge prefers B; humans optional
3. CHumans prefer B; code regressions acceptable if small
4. DHumans and judge both prefer B, agreement is acceptable, and no code evaluator regressedCorrect answer

Why: Step 7 requires all three conditions before promotion.

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1. AExactly two experimentsCorrect answer
2. BUp to five experiments
3. CAny number of experiments
4. DOne experiment

Why: Pairwise annotation queues are built from exactly two experiments so reviewers choose between two outputs for the same input.

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1. ATo reduce token cost
2. BTo counter position bias in the judgeCorrect answer
3. CTo speed up evaluation concurrency
4. DTo enable caching of results

Why: LLM judges often favour the first or second answer by position; randomizing order keeps that bias from deciding the winner.

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1. ABy an unsaved Playground edit
2. BAs a new commit in the Prompt HubCorrect answer
3. CBy a dataset example
4. DBy the production tag

Why: The candidate is a new immutable commit, so exactly what was evaluated is recorded.

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1. ATo bypass rate limits
2. BTo hide examples that failed
3. CTo double the dataset size
4. DTo smooth out non-determinism in app and judge outputsCorrect answer

Why: Repetitions reveal whether outputs and preferences are stable rather than one-off results.

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1. AThe judge's API key
2. BReviewer names only
3. CPrompt commit, model and app versionCorrect answer
4. DOnly the experiment start time

Why: Step 2 records prompt commit, model and app version for reproducibility and audit.

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1. AThe reviewer's previous decision
2. BOnly output B
3. COnly the reference output
4. DThe input and both outputsCorrect answer

Why: The pairwise judge sees the input and both outputs so it can choose between them.

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1. ACount it as a win for B
2. BFlag it as a close or ambiguous case and route it to human reviewCorrect answer
3. CDisable repetitions
4. DDelete the example from the dataset

Why: Split verdicts signal uncertainty that humans should resolve, and may reveal rubric gaps.

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1. ARerun both versions against one pinned dataset versionCorrect answer
2. BTrust the judge to adjust
3. CAverage the two win rates
4. DCompare only the overlapping examples and ship

Why: The workflow must never compare outputs from different dataset versions, so both must run on the same version.

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1. AN
2. BN × RCorrect answer
3. C2 × N × R × R
4. DN + R

Why: Each example in each repetition gets one pairwise judgement; running both orderings would double it.

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1. ALower the number of repetitions
2. BShip based on the judge anyway
3. CDo not gate on the judge; improve it, for example with few-shot examples from human verdicts, and re-checkCorrect answer
4. DRemove the human reviewers

Why: The gate requires acceptable agreement; low agreement means the judge cannot yet be trusted.

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1. AEdit the candidate commit in place
2. BRaise the online sampling rate
3. CDelete the dataset
4. DMove the production tag back to the baseline commitCorrect answer

Why: Tags are movable pointers, so pointing production back at the baseline commit restores the known-good prompt.

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1. AThey ran on the same dataset examplesCorrect answer
2. BThey have the same latency
3. CThey were run on the same day
4. DThey used the same model

Why: Pairwise comparison matches outputs row by row, so both experiments must be run against the same dataset examples, ideally the same pinned version.

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1. AOTEL_PAIRWISE
2. BLANGCHAIN_QUEUE
3. CLANGSMITH_TRACINGCorrect answer
4. DLANGSMITH_JUDGE

Why: Tracing is enabled through LANGSMITH_TRACING per the docs.

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1. A@annotate
2. B@pairwise
3. C@traceableCorrect answer
4. D@experiment

Why: The @traceable decorator records a function call as a run in a trace.

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1. AIt is deleted after scoring
2. BIt is upgraded to extended retentionCorrect answer
3. CIt is moved into a pairwise queue automatically
4. DIt becomes a dataset version

Why: The notes state running an online evaluator upgrades a trace to extended retention.

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1. A@pytest.mark.langsmithCorrect answer
2. B@pytest.mark.trace
3. C@pytest.mark.pairwise
4. D@pytest.mark.eval

Why: The pytest integration uses @pytest.mark.langsmith.

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1. ABecause LangSmith allows only one example at a time
2. BTo randomize answer order
3. CTo extend trace retention
4. DTo avoid provider rate limits and errors that make experiments unevenCorrect answer

Why: Too much concurrency causes throttling and failures that skew coverage and latency metrics.

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1. AThe sampling rate of online evaluation
2. BHow many experiments are compared
3. CHow many independent reviewers must judge each pairCorrect answer
4. DThe judge's temperature

Why: Reviewer count sets how many human judgements each item collects.

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1. APin the production tag
2. BLock the dataset version
3. CTemporarily hold an item for one reviewer so others do not duplicate the workCorrect answer
4. DReserve model capacity for the judge

Why: Reservations stop two reviewers working on the same item at the same time.

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1. AWith the judge's verdict shown first
2. BLabelled baseline and candidate
3. CBlind to which version produced each answerCorrect answer
4. DOnly the output the judge preferred

Why: Step 6 requires reviewers to choose blind to the version to avoid bias.

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1. AIt doubles judge cost but exposes verdicts that flip with positionCorrect answer
2. BIt makes code evaluators unnecessary
3. CIt reduces app calls by half
4. DIt removes the need for a dataset

Why: Both orders give a per-example position-consistency check at twice the judge cost.

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1. AThey overwrite the baseline experiment
2. BThey automatically move the production tag back
3. CThey are discarded
4. DThey are added to the dataset, creating a new version for the next comparisonCorrect answer

Why: Step 8 feeds failing traces back into the dataset for the next comparison.

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1. ARandomized position of answers
2. BWin/tie/loss counts
3. CComparing exactly two experiments
4. DRubrics, assertions and export to datasets on individual runsCorrect answer

Why: Single-run queues offer rubrics, assertions and export to datasets for individual runs.

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1. Aopenai_hook
2. Blangsmith_patch
3. Ctrace_llm
4. Dwrap_openaiCorrect answer

Why: wrap_openai is the LangSmith wrapper for OpenAI clients.

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1. Apromptdiff
2. Botel-judge
3. Cpairqueue
4. DagentevalsCorrect answer

Why: agentevals provides trajectory evaluators for agents.

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1. ATags
2. BThreadsCorrect answer
3. CCommits
4. DReservations

Why: Threads group multi-turn conversations.

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1. AUse a pairwise annotation queue
2. BIt cannot; only LangChain apps are supported
3. CSend to the OTLP endpoint, using a collector to fan outCorrect answer
4. DExport CSV files nightly

Why: LangSmith supports native OTLP ingestion, and a collector can fan out to multiple backends.

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1. AOnly pytest files
2. BOnly hand-written cases
3. CCurated cases, production traces or synthetic dataCorrect answer
4. DOnly synthetic data

Why: The notes list curated cases, production traces and synthetic data as sources.

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1. AThe dataset version
2. BThe prompt commit message
3. CFilters and sampling ratesCorrect answer
4. DThe pairwise queue rubric

Why: Online evaluators are configured with filters and sampling rates.

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