When to use rules vs models?

Rules for stable, simple patterns (regex, lists); models/LLMs for varied natural language. A hybrid approach often works best.

How do I assess quality?

Use stratified test sets and per‑class metrics; monitor drift and confidence in production with human review on critical cases.

Data classification, PII redaction/masking, access controls and minimal retention with audits.

Supercharge processes with classification, entity extraction, summaries, sentiment and translation on Azure AI.

AI for Business Cluster · Back to pillar · Language · Azure ML

Automatic routing, prioritization and SLAs based on content and intents.

Extract customer IDs, products, amounts, dates and PII with rules + models.

Conversation summaries, meeting minutes, email templates and reports.

Reputation analysis, churn signals and prioritization across digital channels.

Automatic content and real‑time conversation translation.

Input → Clean/Detect language → (Rules/Model/RAG) → Action → Feedback → Retraining

Use RAG for broad, up‑to‑date knowledge with citations.

Approach	Pros	Cons	When to use
Rules	Simple, explainable	Limited scalability	Fixed patterns
Classic ML	Good accuracy/cost trade‑off	Feature engineering	Known classifications
LLMs	Language flexibility	Cost/latency, hallucinations	Varied text, generation

Balance classes, handle imbalance and keep borderline examples.

Per‑class metrics, human evaluation rubrics and feedback loops.

PII redaction, audits, explainability and per‑call cost controls.

How to handle multilingual?

Detect language → normalize → translate if needed → apply unified pipeline; use native models for main languages when possible.

Which KPIs to track?

Accuracy/F1 per class, latency P95, confidence score, intent coverage, cost per item.

Do we need a data lake?

Recommended to store text history, version training sets and support audits/compliance.