Final Evaluation and Success Metrics: The Results

We have analyzed the data, built the evaluation set, trained the model in layers, and taught it to handle angry users. Now, it's time for the final exam.

In this final lesson of the TechFlow case study, we will look at the actual performance data of our fine-tuned model compared to the base $7B$ and $70B$ models. We will also learn how to translate these technical numbers into the language of the business: Cost Savings and Customer Satisfaction.

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1. The Head-to-Head Comparison

We ran our $50$-sample Comparative Evaluation Set (from Lesson 2) across three models.

Metric	Llama 3 7B (Base)	GPT-4o (Base)	TechFlow-Agent (7B Fine-Tuned)
Technical Accuracy	42%	88%	94%
Policy Compliance	60%	85%	100%
Brand Tone (1-10)	5.2	7.8	9.4
Inference Cost	$0.05 / 1k req	$15.00 / 1k req	$0.05 / 1k req

• Conclusion: Our fine-tuned $7B$ model actually beat GPT-4o in technical accuracy for our specific software, while being $300\times$ cheaper to run. This is the ultimate "ROI" of fine-tuning.

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2. Business Impact: The "Three Pillars"

When you present your results to your manager or client, focus on these three things:

A. First Contact Resolution (FCR)

By teaching the model "TechFlow" specifics that general models don't know, we reduced the number of tickets that had to be sent to human engineers by $45%$.

B. Scalability

The model handles $10,000$ requests per hour with zero wait time. To do this with humans, TechFlow would have needed to hire $25$ new support staff.

C. Consistency

Unlike human agents who might be tired on a Monday morning or stressed on a Friday afternoon, the fine-tuned model provides the same high-quality, polite, and accurate response every time.

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Visualizing the ROI Curve

graph TD
    A["General Purpose LLM Cost"] --> B["Scales Linearly with Traffic $$$"]
    C["Fine-Tuned Specialized LLM Cost"] --> D["High Upfront / Near-Zero Scaling $"]
    
    B --> E["Profit Margin Squeeze"]
    D --> F["High Profit Scalability"]
    
    subgraph "The Economics of Specialization"
    D
    F
    end

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3. The "Next Steps" for TechFlow

Specialization is never "done."

1. Weekly Audit: Every Friday, we take the $5$ questions the model got wrong and add them to our "Golden Dataset" for the next training run.
2. Multilingual Expansion: In the next phase, we would fine-tune the model on our Spanish and Japanese tickets to expand the brand's global reach.

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Summary and Key Takeaways

• Specialization Wins: A small fine-tuned model can outperform a giant general model on niche tasks.
• Cost Efficiency: Fine-tuning allows you to achieve elite performance at a fraction of the cost of GPT-4.
• Business Language: Don't just talk about "Loss Curves"; talk about "Ticket Deflection" and "Customer Retention."
• Continuous Loop: Use production feedback to constantly improve your training data.

Congratulations! You have successfully completed the first case study. You have seen how to take a raw pile of messy support tickets and turn them into a high-performance, expert AI agent.

In Module 17, we move into an even higher-stakes environment: Case Study: Fine-Tuning for Medical Diagnosis and Reasoning.

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Reflection Exercise

1. Why is a "$100%$ Policy Compliance" score more important than a slightly higher "Tone" score in a professional environment?
2. If your $7B$ fine-tuned model is $10%$ worse than GPT-4o but $500\times$ cheaper, which model would you choose for a free feature in your app? Which would you choose for a paid "VIP" feature?

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SEO Metadata & Keywords

Focus Keywords: fine-tuned vs base model comparison, AI ROI for business, first contact resolution LLM, customer support automation success metrics, scaling specialized AI. Meta Description: Case Study Final Results. See the data-driven proof of how fine-tuning allows a small 7B model to outperform GPT-4o in specialized technical support tasks.