Module Overview
Applying LLMs and vision models inside automation flows: classification, summarization, routing, and extraction. Covers verification, confidence thresholds, and fallback logic to avoid costly errors.
Learning Objectives
- Identify automation steps where AI adds value vs where deterministic logic is preferred.
- Integrate model calls into flows with confidence scoring and fallback paths.
- Design verification steps and human escalation for model outputs.
Lesson-by-Lesson Breakdown
Use-case mapping: classification, summarization, extraction.
Confidence & thresholds: when to accept vs escalate.
Combining deterministic rules with model outputs.
Logging & audit trails for model decisions.
Cost considerations and rate-limit management.
Hands-on Activities & Deliverables
Activities
Build an email triage automation using an LLM to classify and route messages with a fallback human review queue.
📦 Deliverable
Demo flow, sample dataset, and evaluation report (precision/recall + false positive analysis).
Required Tools & Readings
Model API examples (conceptual), decision threshold guides.
Assessment & Rubric
- Correctness of classification/routing40%
- Fallback & verification design30%
- Cost-awareness & documentation30%
Prerequisites
Modules 1–4 recommended.
Parent-Friendly Value
Shows how AI can reduce manual workload while keeping humans in control when needed.