Customer feedback to regression tests

Capture explicit feedback (thumbs, comments) and implicit signals (retries, escalations) linked to run IDs. Automation rules send negative or flagged runs to an annotation queue where reviewers write corrected answers. Corrected examples join a regression dataset with tags. Every prompt, model or code change runs against it in CI, and releases are blocked when previously fixed cases fail again.

Users rephrasing the same question, regenerating answers, copying an answer (a positive signal), abandoning the session, escalating to a human, editing generated content heavily, or following up with 'that's wrong'.

Group feedback by topic or failure type, weigh by frequency, user impact and business value, and fix the clusters that affect the most users or the most important flows first.

Log feedback against the run ID, route negative or low-scored runs to an annotation queue, have experts write the correct answer and label the failure type, add the corrected examples to a dataset, fix the prompt, retrieval or tools, and run the dataset as a regression test before every release.

Evaluators that run automatically on a sample of live production traces, scoring things like correctness, safety or policy compliance without waiting for user feedback. They catch problems most users never report.

Have domain experts write what the ideal answer should contain (key facts, required caveats, format), not necessarily exact wording, so evaluators can judge new answers against the criteria.

Review examples periodically, retire ones whose source facts changed, update references when policies change, and keep tags with dates and sources.

Most users never give feedback; online evaluators score a sample of all traffic for quality signals like relevance and faithfulness, catching problems users do not report.

Make it one click (thumbs up or down) with an optional reason list and comment, ask at the right moment, and never interrupt the user's task.

Return the run or trace ID with each response, send it back with the feedback, and store it with create_feedback, so reviewers see the exact inputs, retrieved context and outputs.

Add the failing case to the regression dataset, run the experiment before and after the fix, confirm the case passes and that other cases did not regress, then watch production feedback on that topic.

Whether the answer was correct, the failure category (retrieval, reasoning, tone, outdated data), the corrected answer or criteria, and whether it should join the regression set.

Online evaluators and user feedback flag poor production runs. You add those failing traces to a dataset, create targeted evaluators, validate the fix with an offline experiment on that dataset, then redeploy, so every real failure becomes a regression test.

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Reviewers go through one run at a time against rubric items, which suits triaging issues and building datasets from production traces. Annotated runs can be exported directly to datasets, and single-run queues also support assertions, free-form acceptance criteria an offline evaluator can grade future runs against.

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Few users give it, and those who do are not representative; some bad answers look convincing, so nobody complains. Combine feedback with online evaluators and regular reviews of sampled traces.

Treat feedback as a signal, not truth: reviewers verify before changing datasets, and abusive or spam feedback is filtered.

Track negative feedback rate over time, time from feedback to fix, regression dataset size and pass rate, and repeat occurrences of fixed issues.

Mask personal data in traces and examples, get consent where required, restrict access to reviewers and apply retention limits.

They filter traces by feedback score, evaluator results or metadata and automatically add them to queues or datasets, so reviewers only see relevant runs.

Tag examples by product area, failure category, source (feedback, audit, synthetic) and date, so experiments can be broken down by slice.

Raw feedback is noisy, biased and sometimes wrong; curated corrections and evaluations improve systems more safely, and most fixes come from prompts, retrieval or tools rather than training.

When a reported issue is fixed, optionally tell affected users, show release notes, and thank users for feedback to encourage more.

On every change in CI for important flows and nightly for the full suite, with results tracked over time.

Configure reservations on the annotation queue so an item is held for one reviewer for a set time, and set how many reviewers each run needs when you want independent second opinions.

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When an online evaluator runs on any run in a trace, LangSmith upgrades the trace to extended data retention. This affects trace pricing but keeps the traces that met your evaluation criteria available for investigation.

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Link feedback to the run tree, then look at intermediate steps for negatively rated runs: retrieval results, tool calls and their outputs, and the final generation. Grouping failures by the step where they went wrong points to the component to fix.

Cluster feedback into themes, fix urgent individual harms immediately, but prioritise work by the size and severity of each cluster, so the team improves the issues that affect most users rather than the loudest complaint.

Thumbs are quick and get more responses; ratings and comments give more detail but fewer responses. Many products use thumbs with an optional reason.

Dashboards of feedback rates by area, top failure categories, fixes shipped and their impact on negative feedback.

Tag feedback with the app and prompt version, so old feedback is interpreted in context and not used to judge the new version unfairly.

A named team or person responsible for triage cadence, dataset quality and release gates, so feedback does not pile up unread.

Record which feedback items each fix addressed, show a short in-product note or changelog entry when relevant, and for enterprise customers mention the improvement in account reviews.

1. AUsers regenerating or rephrasing the questionCorrect answer
2. BCopying the answer
3. CClosing after success
4. DThumbs up

Why: Retries suggest dissatisfaction.

1. ADeleted
2. BIn marketing
3. CIn the regression datasetCorrect answer
4. DIn the system prompt verbatim

Why: Run them before releases.

1. AFirst come only
2. BRandomly
3. CBy length of comment
4. DBy frequency, impact and business valueCorrect answer

Why: Fix the biggest clusters first.

1. AThe run ID of the answerCorrect answer
2. BThe deploy date
3. CThe model's name only
4. DThe user's email address

Why: The run ID connects the feedback to the full trace.

1. ANothing
2. BKey facts, caveats and format criteriaCorrect answer
3. CExact wording only
4. DThe model's output

Why: Evaluators judge against criteria.

1. AThey replace datasets
2. BMost users never leave feedbackCorrect answer
3. CFeedback is always wrong
4. DTo save money

Why: They score sampled traffic.

1. APop-ups that block work
2. BEmail forms only
3. COne click with optional reasonsCorrect answer
4. DA long survey after every answer

Why: Low friction gets more responses.

1. AA developer's opinion
2. BNo new complaints for a day
3. CA bigger model
4. DBefore-and-after experiments plus production feedbackCorrect answer

Why: Check no other regressions.

1. ACorrectness, failure category and correctionCorrect answer
2. BOnly a star rating
3. CNothing
4. DThe user's email

Why: Categories guide fixes.

1. AKept forever
2. BDeleted randomly
3. CIgnored
4. DReviewed and retired when facts changeCorrect answer

Why: Keep dates and sources.

1. ADeleted immediately
2. BIgnored until next year
3. CSent to customers
4. DAdded to a dataset for offline validationCorrect answer

Why: Real failures become regression tests.

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1. ADatabase constraints
2. BUnit tests in code only
3. CUser passwords
4. DFree-form acceptance criteria for future gradingCorrect answer

Why: An offline evaluator can grade later runs against them.

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1. AThe vector store
2. BAnnotation queuesCorrect answer
3. CThe billing page
4. DGit history

Why: Annotation queues hold runs for people to review and label.

1. ATo reduce cost
2. BTo delete old traces
3. CTo train the model
4. DSo mistakes that were fixed do not come backCorrect answer

Why: It acts as a regression test for known failures.

1. AGround truth
2. BSpam
3. CIrrelevant
4. DA signal verified by reviewersCorrect answer

Why: Some feedback is wrong.

1. AMore prompts
2. BFalling negative feedback and repeat issuesCorrect answer
3. CMore tokens
4. DBigger datasets only

Why: Fixed issues should stay fixed.

1. APublished
2. BShared with partners
3. CIgnored
4. DMasked with access limitsCorrect answer

Why: Privacy rules apply to feedback.

1. AAnswer users
2. BSend low-scored runs to queues automaticallyCorrect answer
3. CWrite fixes
4. DTrain models

Why: Reviewers see relevant runs.

1. AArea, failure category, source and dateCorrect answer
2. BColour
3. CReviewer's age
4. DNothing

Why: Enables slice analysis.

1. AIt is noisy and sometimes wrongCorrect answer
2. BTraining is illegal
3. CFeedback is encrypted
4. DIt is too small

Why: Curate and verify feedback first.

1. AYearly
2. BNever
3. COnly after outages
4. DNightly, plus key flows on every changeCorrect answer

Why: Track results over time.

1. ANothing
2. BApp and prompt versionsCorrect answer
3. CBrowser theme
4. DFont size

Why: Interpret it in context.

1. AData export
2. BOnline evaluation
3. CSampling
4. DTwo reviewers judging the same item at onceCorrect answer

Why: Each item is held for one reviewer.

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1. AAn upgrade to extended data retentionCorrect answer
2. BThe trace to be deleted
3. CA new deployment
4. DThe prompt to change

Why: This affects trace pricing.

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1. ADatasetsCorrect answer
2. BProduction code
3. CBilling
4. DPrompts automatically

Why: They become future evaluation examples.

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1. AThe loudest single complaint
2. BAlphabetical order
3. CSubmission time only
4. DCluster size and severityCorrect answer

Why: Fixes should help the most users.

1. AIt is required
2. BIt is private
3. CIt is quick and gets more responsesCorrect answer
4. DIt is detailed

Why: Add optional reasons.

1. ACharging them
2. BTelling them when reported issues are fixedCorrect answer
3. CIgnoring them
4. DDeleting feedback

Why: Encourages more feedback.

1. AEvery user
2. BA named team or personCorrect answer
3. CNobody
4. DThe model

Why: Otherwise feedback piles up.

1. ASpam
2. BAn error
3. CA positive signalCorrect answer
4. DA negative signal

Why: Users kept the answer.

1. AModel weights
2. BPrompt text
3. CFeedback trends and fixes shippedCorrect answer
4. DRaw traces

Why: Show impact over time.

1. AOnly the final answer
2. BThe user's browser
3. CBilling logs
4. DIntermediate steps in the run treeCorrect answer

Why: Failures often start in retrieval or tool calls.