LLM-Powered Ticket Management

Learning Objectives

Design effective prompts for LLMs to perform classification, analysis, and extraction tasks with 70%+ accuracy

Implement few-shot learning to teach LLMs nuanced distinctions (e.g., sentiment ≠ urgency) through examples

Parse and validate LLM outputs robustly, handling markdown wrappers, format variations, and hallucinations

Build multi-dimensional analysis systems that extract multiple attributes (sentiment, urgency, entities) from a single text

Combine AI insights with business logic to create priority scoring systems and decision-making pipelines

Evaluate AI systems systematically using metrics, error analysis, and manual review when ground truth is unavailable

Master the fundamentals of building production-ready AI systems for customer support automation. Through three progressive scenarios set at ShopFlow (an e-commerce company), you'll learn to automate core support operations: classifying tickets into categories, prioritizing them by sentiment and urgency, and extracting structured data from unstructured messages.

You'll work hands-on with Meta-Llama LLMs via HuggingFace to build systems that save 1,040+ hours per month and generate $20,820/month in operational savings. This masterclass teaches not just how to call an LLM API, but how to design prompts that work, validate outputs, handle errors, and evaluate systems without ground truth labels—the real skills needed for AI engineering in production.

Setting: You're a new AI Engineer at ShopFlow, working with Maria (Customer Support Operations Manager) to solve escalating support challenges during peak seasons and crisis situations.

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Business Impact:

Scenario 1: Automate ticket categorization → Save 625 hours/month ($12,500)
Scenario 2: Intelligent prioritization → Increase First Contact Resolution from 45% to 68%
Scenario 3: Auto-extract key data → Save 416 hours/month ($8,320) and eliminate errors

Learning Objectives

Design effective prompts for LLMs to perform classification, analysis, and extraction tasks with 70%+ accuracy

Implement few-shot learning to teach LLMs nuanced distinctions (e.g., sentiment ≠ urgency) through examples

Parse and validate LLM outputs robustly, handling markdown wrappers, format variations, and hallucinations

Build multi-dimensional analysis systems that extract multiple attributes (sentiment, urgency, entities) from a single text

Combine AI insights with business logic to create priority scoring systems and decision-making pipelines

Evaluate AI systems systematically using metrics, error analysis, and manual review when ground truth is unavailable

LLM-Powered Ticket Management

Learning Objectives

Overview

Prerequisites

LLM-Powered Ticket Management

Learning Objectives

Overview

Prerequisites