Recommendation
Start by separating classification from summarization, force a structured JSON output from the model, set the model to deterministic settings (low temperature), and add rule-based fallbacks in Zapier for keywords and low-confidence outputs. That combination will reduce ~10% false negatives quickly and give you traceability to iterate.

Why this works (short)
- Summaries can lose signal; a dedicated classifier (even a short few-shot prompt) is more reliable for priority decisions.
- Structured output + confidence lets Zapier apply deterministic logic (Filters/Paths) instead of trusting free-form text.

Decision criteria (which approach to pick)
- Small team / low budget: Add Zapier Filters/Paths with keyword rules + a JSON-producing prompt with examples. Escalate only when rules/low confidence triggered.
- Mid-sized team: Use a multi-step Zap: classify (GPT → JSON), then summarise. Log all model outputs and add human-in-loop for uncertain cases. Use sample-based monitoring to tweak prompts.
- Large team / high accuracy needed: Consider a fine-tuned classifier or a dedicated ML service (or embeddings+kNN classifier) and keep LLM for summaries/augmentation.

Practical debugging checklist (apply in order)
1) Split steps: 1) Extract priority, 2) extract reasons, 3) produce summary. Don’t try to do both in one free-form completion.
2) Prompt pattern: system: "You are a ticket triage classifier." user: "Return only JSON. Schema: {priority: 'low'|'medium'|'high'|'urgent', confidence: 0-1, reasons: [short strings]}. If uncertain, set priority to 'high' and confidence <0.6." Provide 4–8 labelled examples (few-shot) including borderline cases.
3) Model settings: temperature=0, top_p=0. Use deterministic API endpoints when possible.
4) Enforce output format: ask for strict JSON and validate in Zap: if parser fails, route to human.
5) Add keyword fallback: Zapier Filter or Path that checks raw text for words like "fire", "server down", "data breach", "SEV1", and auto-flag as urgent regardless of model.
6) Confidence rules: If confidence < threshold (e.g., 0.6) or reason list is empty, send to human queue or higher-priority Slack channel.
7) Logging & sampling: Save input + model output + final label to a datastore. Daily sample false negatives and update prompt examples.
8) Retries & debias: If a low-priority is later reopened or reclassified, add that case to your few-shot examples for prompt improvement.
9) Zapier specifics: use “Formatter” to parse JSON, “Paths” or “Filters” to route, and “Code by Zapier” or webhooks to run any custom validation. Use Delay or Buffer for human review steps.

Best-for / Avoid-if
- Best for: teams who need quick wins and traceability without heavy ML investment. Works well when you can label examples and iterate.
- Avoid if: you need near-perfect automation at scale; then invest in a trained classifier / supervised model and keep LLMs for explanations.

Final notes
Start by implementing steps 1–6 and run A/B prompt variants. Track false-negative rate daily; aim to bring it well below 1–2% with combined model+rule approach. If you want, I can draft a JSON-only prompt plus 6 example training cases you can drop into your Zapier ChatGPT step.

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