Human review with annotation queues

I would trace everything with @traceable or wrap_openai into a project, then run an online LLM-as-judge on a sampled slice. An automation rule sends low-scoring runs, negatively rated runs and a small random sample into a single-run annotation queue. The queue has clear instructions, a rubric expressed as feedback keys, and assertions for hard rules. Reservations stop two reviewers from grabbing the same run. For subjective criteria I would set two or more reviewers per run and adjudicate disagreements. Approved runs, with corrected outputs, get exported to a dataset. That dataset gates every prompt commit through evaluate() experiments, and the human labels become few-shot examples to recalibrate the judge. The loop turns production failures into regression tests.

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A run is one traced step, such as an LLM call or tool call. Runs nest into a trace, and traces live in a tracing project. A thread groups the traces of one multi-turn conversation. Online evaluators and humans both attach feedback to runs. Each feedback item has a key, a score or value, an optional comment and its source. An annotation queue holds references to runs plus the rubric, assertions and reviewer count. Approved runs are exported as examples, each with inputs and a reference output, into a dataset tagged with splits. An experiment is one evaluate() or pytest execution of a target over that dataset. Comparing experiments gives you regression results.

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Make export the only path into the regression dataset, and gate that export on review state. A run qualifies only when the required number of reviewers have submitted feedback, their scores agree within the defined rules, or a lead reviewer has adjudicated. Disputed runs go to adjudication and never to export. I would also restrict who can write to the regression dataset, so nobody adds examples straight from a trace. A periodic audit can check every example in the dataset against complete, agreeing reviewer feedback on its source run. Any mismatch gets flagged and removed. Without the gate, one hurried click can put a wrong answer into every future regression test.

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There are two layers. First, a reservation hands a run to one reviewer at a time, so two people never edit the same review at once. Second, each reviewer's scores are stored as separate feedback attributed to that reviewer, not as one shared field that gets replaced. When a run needs three reviewers, you end up with three independent sets of feedback that can be compared. Disagreement then shows up as data instead of disappearing as a lost update. Corrected reference outputs are written only at export time, by an approved person, after agreement or adjudication. That keeps the one truly shared artefact out of the concurrent editing path.

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Build the estimate from your own measurements, not from guesses. Start with production runs per day. Multiply by the fraction the automation rule routes to the queue, which combines low judge scores, negative user feedback, errors and the random calibration sample. Multiply by the reviewers required per run to get reviews per day. Add the expected dispute rate times the adjudication effort. Multiply by the median minutes per review, measured during a pilot. Divide by the productive review hours each person really has. Then add headroom for absences and spikes. If the result is too high, tighten the rule filters, lower the calibration sample or require fewer reviewers on low-risk categories.

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Minimise data at the source. Mask or redact sensitive fields in inputs and outputs before they are traced, so reviewers and datasets never see more than they need. Restrict workspace and queue access to trained reviewers, and keep the number of people with dataset write access small. Remember that export copies data into a long-lived dataset, so apply a second redaction check there. Reference outputs written by reviewers must not add customer details either. Be careful with sampling and extended retention, because they decide how much production data is kept. For regulated data, confirm your hosting option and contractual terms with your compliance team before routing real customer traffic into review.

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One reviewer's judgment on tone, helpfulness or completeness is noisy, and if you build a golden dataset on noise, your regression tests and judge calibration inherit it. Setting the queue's reviewer count to two or three gives you an agreement signal per feedback key. Low agreement tells you the rubric is unclear, not just that the model is wrong. I export runs where reviewers agree directly. Disputed runs go to a lead reviewer, who decides and often updates the rubric wording. I would not apply multiple reviewers to everything, because it multiplies cost. I reserve it for subjective keys and high-risk routes like medical or financial answers, and keep single review for objective checks.

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I treat human review as the ground truth and the judge as a cheap approximation that must be measured against it. Automation rules send a random sample of runs to the annotation queue, not only failures, so I can compute agreement between the judge and the human consensus without bias. When agreement drops, I take adjudicated human examples and add them as few-shot examples to the judge prompt, then version it in the Prompt Hub. I run the revised judge as an experiment over the human-labelled dataset with evaluate() and compare it with the old version. Only a judge that matches humans better gets promoted back to the online evaluator.

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The reservation has a time limit. When it expires, the run goes back to the queue and another reviewer can pick it up. This prevents a stuck run from blocking progress, and it is why reservations are leases rather than permanent locks. The design point is that partial work should not count. Only submitted feedback contributes toward the required reviewer count. I would watch for runs that keep expiring, because they often mean the trace is too long or the rubric is unclear for that kind of case. Those runs may need better instructions or a specialist reviewer instead of endless recycling.

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Define it per rubric key and write it down before reviewing starts. Pass/fail assertions should match exactly, because a split on 'is this factually correct' is a real dispute. For categorical keys I would require exact or majority agreement, depending on risk. For numeric scales, set a tolerance, for example adjacent scores count as agreement while wide gaps do not. Any reviewer comment proposing a different correct output should also trigger adjudication, even if the scores match. Track agreement rates per key over time. A key with chronically low agreement usually means the rubric wording is ambiguous, and the fix is the rubric, not more adjudication.

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Disputed runs go to a lead reviewer, ideally through a separate queue or a filter, so they don't mix with first-pass work. The lead sees the full trace plus every reviewer's feedback and comments, then records a final decision as their own attributed feedback. The original reviews stay in place. The lead's decision, including any corrected output, is what drives export. If the lead can't decide, perhaps because the policy itself is unclear, the run is excluded from the dataset and the question goes to whoever owns the policy. I would also feed adjudicated cases back into reviewer instructions, because recurring disputes show exactly where the guidelines are weak.

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Build the rubric from feedback keys that match what you actually want to measure and later automate, such as correctness, groundedness, helpfulness and tone. Reusing the same keys the online LLM-as-judge writes lets you compare human and judge scores directly. Keep scales small and anchored, with a written example for each level. Use pass/fail assertions for hard requirements, such as 'no policy violation', 'cites a source' or 'tool call arguments valid'. Put clear instructions at the top of the queue, including what counts as a correct output. Keep the rubric short enough to finish in a reasonable time. Each extra key adds review time and is another place reviewers can disagree.

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Human time is the scarcest resource in the loop. The online evaluator scores a sample of production runs cheaply and writes feedback that automation rules can filter on. Humans then spend their time on low-scoring runs, errors and runs with negative user feedback, where they add the most value. The judge also gives you a score to compare against human labels, which shows how much you can trust it. The risk is that humans only see what the judge flags and miss its blind spots. That is why a random calibration sample, chosen independently of judge scores, must also go to the queue.

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Running an online evaluator on a trace upgrades it to extended retention. In practice, the runs that get scored, and so are likely to enter the queue, are kept longer. That is useful, because a run must still exist when reviewers reach it and when you export it. It also has privacy and cost implications, because more production data persists for longer. Export copies inputs and outputs into a dataset, which has its own lifecycle independent of trace retention. So the dataset is where long-lived data really sits. Decide deliberately what may be stored there, and mask or remove sensitive fields before export.

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Splits let one dataset serve different purposes without copying it. Tag exported examples with labels such as regression, edge-case or calibration. Fast CI checks can run on the regression split on every prompt commit, while the larger edge-case split runs before a release or a model swap. Splits also let you track performance per category, so an overall average does not hide a drop on hard cases. Keep examples used as few-shot demonstrations for the LLM-as-judge out of the split used to measure that judge. Otherwise you are grading it on its own training material. Review split membership as the product changes.

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Prompts are versioned in the Prompt Hub with commits and tags, for example a production tag and a candidate tag. When a new commit is made or a model is changed, CI runs evaluate() or the pytest integration against the reviewed dataset. Each experiment's metadata records which prompt commit and model it used. You compare the candidate experiment with the baseline experiment from the current production version, ideally example by example, to see regressions and not just average shifts. Repetitions help with non-deterministic outputs. Only when the comparison passes do you move the production tag to the new commit. That keeps deployment tied to reviewed evidence.

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Detect it by monitoring queue inflow and backlog, with alerts when inflow jumps well above its normal range. Common causes are a judge outage producing default low scores, a broken filter, or a real incident that spikes errors. To recover, pause or narrow the rule first. Then work out whether the flood is real signal, such as a production regression, or noise. For noise, remove the irrelevant runs from the queue instead of making reviewers process them. For a real incident, sample a representative subset for review instead of reviewing every run. Prevent repeats by testing rule filters on recent data before enabling them, and by capping the calibration sampling rate.

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Use the random calibration sample, because it is chosen independently of judge scores. For those runs you have both judge feedback and human feedback under matching keys, so you can track agreement over time. Alert when agreement drops or when the judge becomes systematically harsher or more lenient. Drift often follows a judge model change, a product change that creates new kinds of output, or a prompt change in the app. To fix it, add recent adjudicated human labels as few-shot examples to the judge and rerun it offline against a held-out human-labelled split. Redeploy it as the online evaluator only once agreement recovers.

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Treat the source run ID as the natural key. Record the source run ID in each example's metadata, and before exporting check whether an example from that run already exists in the target dataset. If an automated script performs export, it should look up existing examples first and update instead of inserting. Mark exported runs, for example with a feedback key or tag, so they are not picked up again. Duplicates matter because they quietly give some cases extra weight in regression averages. If a run is re-adjudicated later, update its existing example and let dataset versioning keep the history.

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I'd track queue inflow, backlog size and the age of the oldest unreviewed run, to catch rule misfires and staffing gaps. I'd also watch time to completion per run, reservation expiry rates, inter-reviewer agreement per rubric key, dispute rate and adjudication turnaround. On the output side, track how many examples are exported per split each week and whether they cover the failure categories seen in production. Judge-versus-human agreement on the calibration sample tells you whether automation can be trusted. Finally, confirm that regression experiments actually run on every prompt commit or model change. A dataset nobody runs gives no protection.

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A higher sampling rate gives better coverage and catches rare failures sooner. It also costs more in judge model calls, adds load, and upgrades more traces to extended retention, which affects storage and how long potentially sensitive data is kept. A lower rate is cheaper but may miss low-frequency issues and leaves the automation rule with fewer candidates. I would use filters to apply higher rates to high-risk traffic, such as particular routes, tools or customer segments, and lower rates elsewhere. Then I would check whether the queue receives enough diverse runs. Revisit the rate whenever traffic volume or the judge model changes.

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Threads group the traces of a conversation, and online evaluators can run at thread level, for example judging whether the user's goal was eventually met. A bad single turn may be fine in context, and a sequence of acceptable turns may still fail the user. In the queue, reviewers need the earlier turns to judge a run fairly. So instructions should tell them to inspect the conversation history, and the rubric might include conversation-level keys. When exporting, decide whether an example's input is the full prior history or just the last message. That choice must match how the regression target will be called in evaluate().

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Mark test functions with @pytest.mark.langsmith. Each test case then becomes an example, and each test run becomes an experiment in LangSmith. Tests can load examples from the human-reviewed dataset and call the app with the candidate prompt or model. They assert hard requirements in code and log softer scores as feedback. CI runs the suite on pull requests that touch prompts, model configuration or retrieval logic. Hard assertions block the merge, while score changes appear in the experiment comparison for a human to judge. Caching and concurrency settings keep run times practical. Credentials for the model provider and LangSmith come from CI secrets, never from the repository.

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For an agent, the final answer is only part of the story. Reviewers have to inspect the full trace, including which tools were called, with what arguments, in what order, and whether intermediate steps were sensible. The rubric should include trajectory keys, such as 'used the right tool' or 'no unnecessary steps', not only answer quality. When exporting, you may store a reference trajectory alongside the reference output. Offline, agentevals trajectory evaluators, including a trajectory LLM-as-judge, can compare new runs against those expectations. The human labels on trajectories then help calibrate that judge, the same way answer labels calibrate an answer judge.

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Because feedback is attributed per reviewer, you can measure each person's agreement with peers and with adjudicated outcomes. A consistently harsh reviewer is not necessarily wrong. They may be applying the rubric more strictly, so look at the adjudicated cases first. If they are out of line, run a calibration session using examples with known answers and clarify the rubric anchors. If the rubric itself is ambiguous, fix the wording for everyone. While this is resolved, the multi-reviewer requirement and adjudication protect the dataset, because a lone outlier creates a dispute rather than a bad reference output. Avoid silently discarding someone's scores, since that hides real rubric problems.

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They measure different things and have different reliability. A thumbs-down from a user tells you something about their experience, but it may reflect a valid refusal, a slow response or an unrelated frustration. Reviewer feedback follows a rubric. Mixing them under one key makes it impossible to compute reviewer agreement, to calibrate the judge against human labels, or to see when user sentiment and expert judgement differ. Keeping separate keys lets user feedback act as a routing signal for the automation rule, while reviewer and adjudicated feedback decide what gets exported. The judge's scores should also stay under their own source, so you always know who said what.

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Very little downstream. LangSmith accepts traces through its OTLP endpoint, so spans become runs in a tracing project, and the online evaluators, automation rules and queues work the same way. The main work is making sure span attributes map to useful inputs, outputs and metadata, so reviewers can read the trace and filters can match on it. If you also send telemetry to other backends, use an OpenTelemetry collector to fan out to LangSmith and your existing observability tools. Put redaction in the collector or the app, before data leaves your environment. Check that multi-turn conversations carry a consistent thread identifier so threads are grouped correctly.

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Use a single-run queue to judge individual production runs against a rubric and assertions and to build reference outputs, which is the core of this workflow. Use a pairwise queue when the question is relative: which of exactly two experiments produced the better output for the same input, for example when comparing a candidate prompt or model with the current one. Pairwise judgements are often easier and more consistent for humans than absolute scores, especially for subjective qualities like tone. But they don't directly give you reference outputs. Many teams use both: single-run review feeds the dataset, and pairwise review helps decide between close experiments.

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Never lose the original. The production run keeps the model's actual output. Reviewer feedback and comments, attributed to each person, explain why it was judged wrong. The corrected output appears only in the dataset example, together with metadata linking back to the source run and recording who approved it. Dataset versioning keeps earlier versions if a reference is later changed. With this setup you can always answer three questions: what did the model say, who judged it, and who decided the reference. That matters for debugging regressions, for resolving disputes about the rubric, and for any compliance review of how evaluation data was produced.

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First identify the affected examples, using metadata such as category or tags, or by running an evaluator that checks against the new policy. Don't edit them silently. Route them back through review, ideally the same queue with updated instructions, so corrections get the same multi-reviewer and adjudication safeguards. Update the examples through a new dataset version, so old experiments remain interpretable against the version they used. Temporarily exclude disputed examples from the regression split rather than letting a mix of old and new policy produce confusing results. Afterwards, re-run the baseline experiment, so comparisons start from the updated references.

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Split the human-labelled data. One part supplies few-shot examples for the judge prompt. A separate held-out part, never shown to the judge, is used to measure its agreement with humans. If the same examples appear in both, measured agreement will look better than real-world performance. Also keep the few-shot set diverse across categories and score levels, so the judge does not learn that, say, short answers always fail. Version the judge prompt in the Prompt Hub, so you know which few-shot set produced which agreement numbers. Refresh both sets periodically with newly adjudicated labels as production behaviour changes.

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Automation rules can trigger webhooks, and dashboards support alerts, so the loop can connect to existing tools. Examples include notifying a lead reviewer's channel when disputed runs pile up, or opening a ticket when the online judge's score drops sharply on a route. You can also alert when queue backlog or the age of the oldest unreviewed run passes a threshold your team chooses. Webhook receivers should authenticate requests, handle duplicate deliveries idempotently, and avoid copying full trace contents into chat tools. Send a link to the run instead, so access control stays in LangSmith.

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Base it on risk and on measured agreement. High-stakes categories, such as medical, financial or legal answers, or runs destined for the regression split, deserve more reviewers, so one mistaken opinion can't become truth. For low-risk categories with historically high agreement, fewer reviewers may be enough, with spot checks. Start higher during a pilot to measure agreement. Then lower the count only for keys and categories where agreement is reliably strong. Remember that the count multiplies review workload directly, so it is one of the main levers in the staffing estimate. You can split traffic into separate queues with different reviewer counts.

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1. APrompt Hub tags
2. BDataset versioning
3. CThe reservation given to the reviewer who opens the runCorrect answer
4. DThe online evaluator's sampling rate

Why: A reservation hands a run to one reviewer, so no one else works on it until the reservation is released or expires.

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1. AThe run stays locked forever
2. BThe run is deleted
3. CThe run is exported automatically
4. DThe reservation expires and the run returns to the queueCorrect answer

Why: Reservations expire, which returns the run to the queue for another reviewer.

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1. AAs feedback on the run, attributed to that reviewerCorrect answer
2. BIn the prompt commit message
3. CAs a new tracing project
4. DOnly in the reviewer's browser

Why: Review results are stored as feedback on the run, attributed to the reviewer.

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1. ADelete the third reviewer's feedback
2. BLet the LLM-as-judge decide and export
3. CExport immediately with the majority score
4. DSend the run to a lead reviewer for adjudicationCorrect answer

Why: Disputed runs go to a lead reviewer, and disputed runs must never become reference outputs without adjudication.

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1. ATo hold a run for one reviewer so others do not work on it at the same timeCorrect answer
2. BTo lock the dataset while an export runs
3. CTo reserve LLM-as-judge capacity for the queue
4. DTo guarantee a run is stored with extended retention

Why: A reservation assigns a queued run to a single reviewer for a period and releases it if they do not finish. This prevents duplicate or conflicting reviews.

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1. ASingle-run queue with assertions
2. BOnline evaluator with a pairwise template
3. CPairwise annotation queueCorrect answer
4. DAutomation rule with a webhook

Why: Pairwise annotation queues are built for comparing exactly two experiments. Single-run queues grade individual runs against a rubric.

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1. ALANGSMITH_TRACINGCorrect answer
2. BLANGSMITH_QUEUE
3. COTEL_DATASET
4. DLANGCHAIN_REVIEW

Why: LANGSMITH_TRACING enables tracing, together with @traceable or wrappers such as wrap_openai.

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1. AA pytest marker
2. BAn automation ruleCorrect answer
3. CA Prompt Hub tag
4. DA dataset split

Why: Automation rules match runs by filters, such as low judge scores, and add them to the annotation queue.

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1. ATo reduce trace retention
2. BTo measure the judge on runs it did not flag and catch its blind spotsCorrect answer
3. CTo avoid writing feedback
4. DTo satisfy the pairwise queue's two-experiment limit

Why: Runs selected only by the judge hide its blind spots, while an independent random sample allows unbiased judge-versus-human comparison.

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1. AThe trace is deleted after scoring
2. BThe trace is upgraded to extended retentionCorrect answer
3. CThe trace becomes a dataset example
4. DThe trace is moved to a pairwise queue

Why: Running an online evaluator upgrades a trace to extended retention.

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1. AAn automation rule
2. BA tracing project
3. CA thread
4. DA dataset of examplesCorrect answer

Why: Datasets hold examples made of inputs and reference outputs, and experiments run against them.

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1. AExactly three
2. BOnly one
3. CExactly twoCorrect answer
4. DAny number

Why: Pairwise queues compare exactly two experiments.

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1. A@pytest.mark.trace
2. B@pytest.mark.langsmithCorrect answer
3. C@pytest.mark.queue
4. D@pytest.mark.judge

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

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1. AUse it as the reference output of the exported dataset example, keeping the original run unchangedCorrect answer
2. BStore it in the online evaluator's prompt
3. CAdd it to the tracing project name
4. DOverwrite the production run's output

Why: Corrections become the example's reference output at export, while the original run preserves what the model actually said.

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1. AMedian minutes per review
2. BThe number of prompt tags in the Prompt HubCorrect answer
3. CRuns routed to the queue per day
4. DReviewers required per run

Why: Workload depends on routed volume, reviewers per run, dispute rate and review time, not on how many prompt tags exist.

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1. AExport it with the corrected output as the reference outputCorrect answer
2. BConvert it into an online evaluator rule
3. CExport the original model output as the reference output
4. DSkip exporting it because failed runs do not belong in datasets

Why: A failed run with a corrected reference output is exactly the regression case a dataset needs. Exporting the original wrong output would make tests reward the bug.

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1. AA threadCorrect answer
2. BA rubric
3. CA commit
4. DA split

Why: Threads group multi-turn conversations.

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1. Awrap_openai()
2. Bexport()
3. Ctraceable()
4. Devaluate()Correct answer

Why: evaluate() runs experiments and supports repetitions, concurrency and caching.

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1. AExport traces to a dataset first
2. BIt cannot be done
3. CRun two copies of the app
4. DUse an OpenTelemetry collector to fan out, with LangSmith's OTLP endpoint as one destinationCorrect answer

Why: LangSmith supports native OpenTelemetry through an OTLP endpoint, and a collector can fan out to several backends.

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1. AFeedback keysCorrect answer
2. BPrompt tags
3. CDataset splits
4. DOTLP span names

Why: The single-run queue rubric is defined from feedback keys, which reviewers score.

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1. AThrough reservation IDs
2. BThrough queue reviewer counts
3. CThrough retention tiers
4. DThrough Prompt Hub commits and tagsCorrect answer

Why: Prompts are versioned in the Prompt Hub with commits and tags.

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1. APrompt Hub
2. BagentevalsCorrect answer
3. Cwrap_openai
4. DVitest

Why: agentevals provides trajectory evaluators, including a trajectory LLM-as-judge.

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1. AUse a pairwise queue
2. BRaise the sampling rate
3. CRecord the source run ID on each example and check for it before exportingCorrect answer
4. DRequire more reviewers

Why: Using the source run ID as a natural key makes export idempotent and avoids duplicate examples.

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1. AExport all queued runs
2. BDelete the dataset
3. CAsk reviewers to work faster
4. DPause or narrow the rule, then investigate the causeCorrect answer

Why: Stop the inflow first, then decide whether the spike is a real incident or a misconfigured filter or judge.

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1. ABecause pairwise queues require it
2. BBecause few-shot examples cannot be stored in datasets
3. CTo reduce trace retention
4. DTo avoid leakage that inflates measured judge accuracyCorrect answer

Why: Testing the judge on its own demonstrations overstates how well it agrees with humans.

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1. ARedact only after export
2. BRely on reservations to hide data
3. CRaise the sampling rate
4. DRedact sensitive fields before they are tracedCorrect answer

Why: Minimising data at the source means reviewers, retained traces and datasets never hold more than needed.

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1. AReviewer agreement and judge calibration become impossible to measure cleanlyCorrect answer
2. BReservations stop working
3. CExports fail
4. DTraces lose extended retention

Why: Mixing sources with different reliability under one key hides who judged what, which breaks agreement and calibration analysis.

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1. AFalling agreement between judge and human scores on the random calibration sampleCorrect answer
2. BMore prompt commits
3. CMore dataset splits
4. DShorter reservation times

Why: The calibration sample has both judge and human labels, chosen independently of judge scores, so changes in agreement reveal drift.

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1. ALeave them and ignore failures
2. BEdit them silently in place
3. CRe-review the affected examples through the queue and update them as a new dataset versionCorrect answer
4. DDelete the whole dataset

Why: Re-review keeps the multi-reviewer safeguards, and versioning keeps older experiments interpretable.

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1. ASetting reservation timeouts
2. BGrouping conversation turns
3. CLimiting reviewer access
4. DRunning or reporting experiments on subsets of a datasetCorrect answer

Why: Splits tag examples so experiments can target or report on specific subsets.

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1. Atraceable_queue
2. Botlp_wrap
3. Clangsmith_judge
4. Dwrap_openaiCorrect answer

Why: wrap_openai wraps the OpenAI client so its calls are traced.

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1. AProduction runs or threads, with filters and sampling ratesCorrect answer
2. BOnly datasets
3. COnly prompt commits
4. DOnly pairwise queues

Why: Online evaluation runs on production runs or threads, with filters and sampling rates.

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1. AOnly production traces
2. BCurated cases and synthetic dataCorrect answer
3. COnly pairwise queue outputs
4. DOnly OTLP spans

Why: Datasets can be built from curated cases, production traces or synthetic data.

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