Summarize common issues across support tickets with evidence
Autor: AILesson10 min de preparaciónProbado con:ChatGPTRevisado el: 2026-08-28
Respuesta rápida
Group comparable ticket evidence while preserving denominators, uncertainty, duplicates, and severity. Proporciona: Ticket dataset, Data quality and exclusions, Analysis decision. Resultado esperado: An issue taxonomy and evidence table with counts, rates, trends, examples, bias notes, and investigation priorities.
1
Añade tu contexto
Tu texto permanece en este navegador. AILesson Prompts no lo envía a un modelo ni a un servidor.
2
Tu prompt
Los campos sin rellenar permanecen visibles como marcadores de posición, para que puedas copiar y editar el prompt
Analyze common support issues from the supplied ticket dataset without overstating what tickets represent.
Ticket rows, IDs, dates, products, versions, channels, symptoms, outcomes, severity, and window:
[tickets]
Duplicate, missing, merged, bot, privacy, taxonomy, and collection rules:
[quality]
Question, dimensions, periods, thresholds, stakeholder, format, and decisions:
[analysis]
Validate row and unique-case counts, date boundaries, exclusions, duplicates, missingness, and taxonomy consistency before grouping. Preserve original issue text and map it to a transparent issue hierarchy; do not merge cases solely because words look similar. Distinguish symptom, suspected cause, confirmed cause, request type, and outcome. For each theme report unique cases, denominator, rate, severity mix, affected version or segment where supported, resolution status, representative anonymized locators, and confidence. Compare periods only with compatible collection rules and adequate denominators; calculate absolute and relative changes correctly. Do not infer customer prevalence, product defect, root cause, sentiment, or business impact from ticket volume alone. Flag channel, exposure, self-selection, repeat-contact, and release effects. Rank investigation priorities using explicit volume, severity, growth, uncertainty, and fixability criteria. End with validation samples, queries, and facts needed before action.
Privado de forma predeterminadaLa preparación del prompt se realiza localmente en tu navegador. Evita introducir información confidencial en cualquier servicio de IA, a menos que tu organización lo permita.
De la entrada al resultado
Un ejemplo completo
Mira cómo un contexto concreto convierte esta receta en un resultado que puedes usar
Entrada real
Ticket dataset
Aug 1-14 export tickets: 24 rows, IDs A01-A20 plus four follow-up rows sharing A03, A07, A07, A12. Of 20 unique cases: 8 blank PDF charts on Desktop 4.2, 3 blank charts with version missing, 4 CSV encoding complaints, 2 requests for scheduled export, 3 unrelated questions. Severity: blank charts 2 S2 and 9 S3; CSV all S3. Outcomes: 7 blank-chart cases used Firefox workaround, 2 unresolved, 2 unknown. Jul 15-28: 10 unique export cases, but chat tickets were not imported then; 3 blank-chart cases. Desktop 4.2 released Aug 2.
Data quality and exclusions
Deduplicate by case ID. Exclude follow-up rows from case count but retain attempts. Redact customer names. Version missing in 5/20 August cases. Taxonomy: Export > PDF rendering / CSV encoding / Feature request / Other. A known incident links 6 of the 11 August blank-chart cases; cause still under investigation. Chat import began Aug 1 and contributed 6 August cases.
Analysis decision
Support and product weekly review. Identify themes and investigation priority. Compare periods only if defensible. Output a compact table and cautions. Priority weights: severity high, then unique volume, recent growth, uncertainty. Minimum three cases for a theme.
Ejemplo de salida
Validation: 24 August rows reduce to 20 unique cases after four follow-up rows are linked by case ID. Follow-ups remain in attempt history. Five of 20 cases lack version; six cases arrived through a channel absent from July, so raw period comparison is not like-for-like.
Theme | Unique cases / 20 | Severity | Status and evidence | Confidence
PDF blank charts | 11 / 20 (55%) | 2 S2, 9 S3 | 7 workaround, 2 unresolved, 2 unknown; cases A01-A20 should be listed internally by mapped IDs | High on symptom count; medium on version association
CSV encoding | 4 / 20 (20%) | 4 S3 | Resolution details not supplied | High on count, low on outcome
Other themes | below 3-case threshold | mixed/unknown | 2 scheduled-export requests and 3 unrelated questions remain visible but are not labeled common themes | High
Priority 1: investigate PDF rendering because it has the largest unique volume and the only S2 cases. Six of 11 link to the known incident, but that association does not confirm one root cause for all 11. Eight explicitly report Desktop 4.2; three have missing version, so do not call it a 4.2-only defect.
Period caution: blank-chart cases rose from 3 of 10 in July to 11 of 20 in August, an observed +8 cases and +25 percentage points, but chat import began Aug 1 and the 4.2 release changed exposure. Do not report a trend until July chat backfill or a stable-channel comparison is available. Next checks: sample all 11 mappings, obtain missing versions, separate known-incident matches, inspect exposure by active 4.2 users, and recover CSV outcomes.
Por qué funciona
1
Unique-case denominators prevent repeat contacts from masquerading as more affected customers.
2
Symptom-versus-cause separation avoids turning support labels into defect conclusions.
Comprueba el resultado
Are counts based on documented unique-case and exclusion rules?
Are symptom, suspected cause, confirmed cause, and outcome separate?
Do comparisons preserve compatible denominators and collection methods?
Úsalo con confianza
Preguntas frecuentes
Respuestas prácticas sobre cuándo usar esta receta, qué debes proporcionar y en qué casos la revisión humana sigue siendo importante
What should I prepare before using “Summarize common issues across support tickets with evidence”?
For “Summarize common issues across support tickets with evidence,” prepare Ticket dataset, Data quality and exclusions, and Analysis decision. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.
When is the “Summarize common issues across support tickets with evidence” result not ready to use?
The result is not ready if it does not yet deliver the stated outcome—An issue taxonomy and evidence table with counts, rates, trends, examples, bias notes, and investigation priorities—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.
Which AI tools have recorded tests for “Summarize common issues across support tickets with evidence”?
The published test record for “Summarize common issues across support tickets with evidence” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.
Pasa de entender la IA a poner manos a la obra. Practica cómo dar instrucciones, revisar y mejorar resultados con lecciones interactivas para el trabajo y la vida cotidiana.
ChatGPT
