Best low-cost way to triage support with ChatGPT

Short answer
Start with a small, prompt-driven classifier on the ChatGPT API (gpt-3.5 series for bulk triage, gpt-4 for escalations) and add a simple human-in-the-loop review queue. Prototype quickly in the ChatGPT UI to lock prompts, then switch to the API and a tiny worker (serverless function) for automation and cost control.

Why this approach
- API + small worker avoids Zapier fees and gives full control over batching/rates.
- Prototyping in the ChatGPT UI saves time so you don’t waste engineering hours iterating prompts.
- Use cheap model for the steady stream and reserve high-cost models for ambiguity/escalation.

Recommendation
Prototype in the ChatGPT UI to design a short, deterministic classification prompt and label set. When you have stable prompts, move to the API and run classification in batches with caching and a human review queue for low-confidence results.

Decision criteria (when to use UI vs API)
- Use UI if: ~50–100 tickets/day, need automatic routing, or integrations with your helpdesk/DB. Also choose API when you need predictable throughput, batching, and cost control.
- Use cheap model (gpt-3.5-turbo) if throughput and cost matter; use gpt-4 only for complex multi-turn or escalation decisions.

Best-for / Avoid-if
- Best for: early-stage teams needing a low-cost automated triage that improves with human feedback. Good when you need quick routing labels (issue type, priority, SLA, required owner).
- Avoid if: you need 100% deterministic rules-only routing (use rule engine) or you have extremely sensitive PII that can’t go to third-party models.

Practical checklist to implement (minimal viable triage)
1) Define labels and priorities: keep it small (e.g., bug/feature/account/billing; P0/P1/P2).
2) Build deterministic prompt: system message + examples + output format (JSON or single token labels). Use low temperature for consistency.
3) Prototype in ChatGPT UI: test 50–200 real tickets, refine prompts and edge-case examples.
4) Move to API worker: serverless function (Cloud Run/Functions/Lambda) that accepts webhook from your ticketing system and batches tickets.
5) Batch & compress: send multiple tickets per request (concatenate) if context allows to reduce calls.
6) Confidence & fallback: have model return confidence or use token-limited label enforcement; if low confidence, push to “human review” queue.
7) Cache & dedupe: if similar tickets repeat, reuse recent classification to save calls.
8) Escalation path: route low-confidence or P0 to human+gpt-4 review.
9) Monitoring: track throughput, cost per ticket (avg tokens * $/1k tokens), accuracy vs human labels, and false negatives.
10) Iterate: add label examples from human review to prompt/template.

Cost-control tips
- Use smallest model that meets accuracy needs.
- Batch tickets and strip unnecessary text (only include subject + 1–2 lines).
- Use deterministic output (single-word labels) so parsing is trivial.

Tools to consider
Start with ChatGPT (UI -> API). Claude is a reasonable alternative when long context or more conservative analysis is needed.

If you want, I can: (1) draft a compact classification prompt and JSON schema for your labels, and (2) estimate cost given your average ticket length and daily volume.

Compare ChatGPT and Gemini