Recommendation (short): Use a hybrid pipeline: lightweight classifier (embeddings+k-NN or small supervised model) for fast, high-precision prelabeling, then call ChatGPT via the API to 1) finalize label + canonicalize output and 2) produce an explanation. Route low-confidence items to humans and feed those corrections back into retraining. This approach gets you to 90%+ accuracy without extreme costs.

Decision criteria (pick what matters):
- Budget: if limited, prefer embeddings + a simple classifier (cheap) + occasional GPT calls. If you can pay for more API usage, few-shot GPT or fine-tune with ChatGPT can reduce human review.
- Label availability: if you have thousands of labeled emails, train a discriminative model. If you have few labels, use few-shot GPT with retrieval of examples.
- Latency: if you need sub-second throughput, avoid calling the LLM for every email; use hybrid.
- Team size / stage: early-stage teams should iterate with human-in-loop; large ops can automate more aggressively.

Architecture (practical):
1) Ingest: mail server -> preprocessing (strip signatures, normalize dates/URLs, tokenize). Keep raw copy.
2) Candidate retrieval (optional but recommended): compute embeddings for the incoming email and retrieve k=5 nearest labeled examples. This powers few-shot context and helps calibration.
3) Classifier step:
- Option A (cheap + accurate): embeddings -> logistic regression / small NN / k-NN. If confidence >= threshold (0.85–0.9), accept and push to CRM.
- Option B (higher quality): Send system + few-shot prompt to ChatGPT API including retrieved examples and ask for JSON output. Use when classifier is low-confidence.
4) Post-process: normalize labels, output required CRM fields, attach LLM explanation and confidence.
5) Human-in-loop: low-confidence or critical topics go to agents. Capture corrections.
6) Monitoring & retraining: daily/weekly evaluate, add corrected samples back to training store.

Example system + user prompt (API):
System: "You are a support routing assistant. Given an email, choose one label from: [Billing, Technical, Account, Sales, Cancellation, Other]. Return EXACT JSON and a confidence score 0.00–1.00."
User (few-shot): include 3–5 examples: each with raw email and expected JSON. Then final example: "Email: nReturn JSON only."
Expected JSON: {"label":"Technical","subcategory":"Login","confidence":0.92,"tags":["password-reset"],"explanation":"Short reason why."}

Prompt tips for 90%+:
- Provide clear taxonomy and canonical labels. Merge ambiguous ones (e.g., Billing vs Refund) if confusion is high.
- Provide 5–10 high-quality examples per label for few-shot. Use retrieved similar examples.
- Use JSON schema enforcement (ask LLM to return ONLY JSON). Validate schema in code.
- Calibrate confidence: compare model-provided confidence against empirical accuracy, and lower acceptance threshold if overconfident.

Evaluation targets & data needs:
- Holdout test set of 1–2k emails. Track accuracy, precision/recall per class, and confusion matrix.
- Expect: with a hybrid approach and good taxonomy, 90%+ overall accuracy is achievable. If classes are subtle, you’ll need 1k+ labeled samples per confusing class.

Practical rollout checklist:
- [ ] Define and document taxonomy + CRM field mappings.
- [ ] Label a representative sample (500–2,000 emails).
- [ ] Implement preprocessing + deduplication.
- [ ] Build embeddings/retrieval and a lightweight classifier.
- [ ] Create ChatGPT prompt templates and JSON schema.
- [ ] Implement confidence thresholds & human fallback.
- [ ] Monitoring: daily logs, weekly retrain schedule.

When to avoid LLM-only: if latency or cost per email is critical, or you need strict determinism. Best-for LLM-heavy: ambiguous messages, natural-language nuance, and when you want explanations attached.

If you want, I can draft the exact few-shot prompt with 6 examples tailored to your real labels and an implementation checklist for your stack.

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