Analyzing Support Tickets for High-Value Patterns: The Data Audit

Welcome to the Capstone Project phase of this course. In these final modules, we are going to build a comprehensive, production-ready AI agent for a fictional company: "TechFlow," a SaaS platform with complex technical documentation and a high volume of customer support tickets.

The first step in any real-world fine-tuning project isn't training—it's Analytics. You can't train a model to be a "Great Support Agent" if you don't know what "Great Support" looks like in your specific company.

In this lesson, we will look at how to audit thousands of raw tickets to find the high-value patterns that will form our training data.

1. Defining "Success" in Support

In our TechFlow case study, we defines a "Golden Ticket" as one that:

Resolved the Issue on the first try (FCR - First Contact Resolution).
Used a Polite/Professional Tone that matches the brand voice.
Followed the Technical Docs exactly without making up new rules.
Avoided Escalation to a human manager.

We want to find these specific tickets and use them as our Assistant Responses for fine-tuning.

2. Clustering: Finding the "Volume Boosters"

If $80%$ of your tickets are about "How to reset my password," you should focus your fine-tuning on that.

Step 1: Use an LLM or an embedding model (like text-embedding-3-small) to cluster your tickets into 10-20 main topics.
Step 2: Identify the clusters with the highest "Dissatisfaction Score." These are your "Pain Points."
Step 3: Curate 100 perfect examples of how to solve those pain points.

Visualizing the Curation Funnel

graph TD
    A["50,000 Raw Support Tickets (CSV)"] --> B["Automatic Filter: Resolved=True & Rating=5"]
    B --> C["Topic Clustering (AI Analytics)"]
    
    C -- "Topic: API Auth" --> D["Curate 100 Examples"]
    C -- "Topic: Billing" --> E["Curate 100 Examples"]
    C -- "Topic: UI Bugs" --> F["Curate 100 Examples"]
    
    D & E & F --> G["Golden Training Dataset (JSONL)"]
    
    subgraph "The Quality Control"
    G
    end

3. Dealing with "Messy" Data

Real support tickets are ugly. They contain typos, angry emojis, and long signatures with phone numbers.

Typos: Keep them in the User Query (to make the model resilient), but fix them in the Assistant Response (to make the model sound professional).
PII: Use the Presidio scrubbing techniques from Module 12 to remove real customer names before the data reaches the training set.

4. The "Reasoning" Label

For TechFlow, we aren't just training the model to answer; we are training it to Think.

Original Ticket: User asks a question, Agent provides the answer.
Our Fine-Tuning Row: We add a reasoning block at the start: "Thought: The user is asking about API limits. I should check the 'Pro Tier' documentation before answering."

This is called Chain-of-Thought (CoT) fine-tuning, and it makes the model significantly more reliable on technical topics.

Summary and Key Takeaways

Auditing is the foundation of the case study.
Volume vs. Value: Focus on the most common and most difficult tickets.
Clean Response: The user query can be messy, but the training label (Assistant) must be perfect.
CoT: Add "Thinking" steps to your data to improve the model's logic.

In the next lesson, we will move from data to evaluation: Building a Comparative Evaluation Set for Support.

Reflection Exercise

If you find a ticket where the agent was very rude but fixed the problem instantly, should you include it in your training data? (Hint: Think about 'Brand Alignment' in Module 4).
Why is "Topic Clustering" better than just picking the 100 most recent tickets? (Hint: What if the last 100 tickets were all about a temporary server crash?)

SEO Metadata & Keywords

Focus Keywords: customer support fine-tuning case study, analyzing support tickets for AI, golden dataset curation, tech support AI training data, chain of thought support bot. Meta Description: Case Study Part 1. Learn how to audit and analyze raw support tickets to identify high-value patterns for building a specialized, professional customer support agent.