Building a Comparative Evaluation Set for Support

In our TechFlow case study, we have our "Golden Dataset" for training. But before we start the GPU, we need to know how we will measure success.

As we learned in Module 10, general metrics (BLEU/ROUGE) are useless for empathy and complex troubleshooting. We need a Comparative Evaluation Set—a private list of 50 high-stakes support scenarios where our new model will be judged against the baseline model (e.g., GPT-3.5 or raw Llama 3).

In this lesson, we will build the evaluation framework that will tell us if our "TechFlow Agent" is ready for prime time.

1. Defining the Evaluation Dimensions

For a support agent, we grade on four specific criteria:

Resolution Logic: Did the model correctly identify the technical root cause? (e.g., "The user is using an outdated API key.")
Brand Tone: Did the model sound like "TechFlow" (Helpful, energetic, but clinical and precise)?
Policy Compliance: Did the model follow company rules? (e.g., "Do not offer refunds for monthly plans.")
Action Selection: Did the model suggest the correct tool or next step?

2. Creating "Failing" Baseline Samples

To prove your fine-tuned model is better, you need to find examples where the Base Model fails.

Base Model Failure: "I'm sorry, I don't have information about TechFlow's proprietary 'Flux-Routing' system."
Fine-Tuned Target: "The Flux-Routing system is currently being updated. You should check your config.yaml to ensure the version is set to 2.1."

Include 10 of these "Blind Spots" in your evaluation set. This is where your ROI (Return on Investment) will be most visible.

Visualizing the Comparison Loop

graph TD
    A["Adversarial Support Query"] --> B["Base Model Response"]
    A --> C["Fine-Tuned TechFlow Model"]
    
    C --> D["Specific Technical Answer"]
    B --> E["Generic AI Fluff"]
    
    D & E --> F["LLM Judge (GPT-4o)"]
    G["TechFlow Brand Rubric"] --> F
    
    F --> H{"Winner Declared"}
    H -- "FT Wins" --> I["DEPLOYMENT READY"]
    H -- "Base Wins" --> J["RETRAIN (Go back to Module 5)"]

3. Implementation: The Support Judge Prompt

We will use the LLM-as-a-Judge pattern from Module 10, tailored specifically for TechFlow’s metrics.

def techflow_judge_prompt(query, response):
    return f"""
    You are the Head of Support Quality at TechFlow.
    Grade this assistant's response to the User Query.
    
    [User Query]: {query}
    [Assistant Response]: {response}
    
    [TechFlow Policy]: 
    1. Always provide code snippets in Python.
    2. Never promise a refund without a manager.
    3. Always cite the 'Technical Docs v1.5'.
    
    Grading (1-5):
    Accuracy: (Score)
    Tone: (Score)
    Policy: (Score)
    
    Justify your score:
    """

4. Why "Side-by-Side" (SxS) is Essential for Executives

If you show a CEO a "Perplexity Score of 1.4," they won't care. If you show them a Side-by-Side Comparison where the general AI gives a boring answer and your fine-tuned AI gives a perfect, document-citing solution, you will get the budget to scale your project.

Summary and Key Takeaways

Domain-Specific Metrics: Grade on policy and logic, not just grammar.
Blind Spots: Intentionally test the model on things the base model doesn't know.
Executive Presence: Use Side-by-Side comparisons to demonstrate the "Magic" of fine-tuning to non-technical stakeholders.
The Goal:land a "Win Rate" of $>80%$ against the base model before moving to production.

In the next lesson, we will start the actual training journey: Iterative Fine-Tuning: From "Friendly" to "Technical Expert".

Reflection Exercise

If you are fine-tuning a model for a bank, and its "Resolution Logic" is perfect but its "Brand Tone" is rude, is it ready for production?
Why should you update your evaluation set every month? (Hint: Does your product's software stay the same, or do new bugs and features appear?)

SEO Metadata & Keywords

Focus Keywords: customer support AI evaluation, side-by-side model comparison, LLM judge for support bot, evaluating technical accuracy AI, brand tone alignment metrics. Meta Description: Case Study Part 2. Learn how to build a rigorous comparison framework to prove that your fine-tuned support agent is smarter, safer, and more aligned than a general base model.