Iterative Fine-Tuning: From "Friendly" to "Technical Expert"

In our TechFlow case study, we don't just run one training job and call it finished. Professional AI engineering is Iterative.

If you try to teach a model complex technical troubleshooting (e.g., "Debug this SQL query performance") at the same time as you are teaching it a new brand voice (e.g., "Sound like a futuristic surfer"), the model will often struggle to do both. The conflicting signals in the gradient (Module 12) will wash each other out.

The best strategy is the Wisdom Ladder: Layering your training in stages.

In this lesson, we will see how to move from a model that is merely "Polite" to one that is a "Subject Matter Expert."

1. Stage 1: The "Etiquette" Pass (SFT)

Goal: Fix the model's personality.
Data: 500 examples of standard support greetings, sign-offs, and common "Easy" questions (e.g., "How do I change my email?").
Result: The model no longer sounds like an AI; it sounds like a TechFlow employee.
Risk: Low. This stage is very stable.

2. Stage 2: The "Expert Knowledge" Pass (SFT + Document Retrieval)

Goal: Focus $100%$ on technical accuracy.
Data: 1,000 examples of complex troubleshooting. We use the documentation snippets as the "Context" (Module 14).
Result: The model can now solve deep technical bugs that a general model would fail at.
Risk: Moderate. You might see the model become "colder" or more "robotic" as it focuses intensely on the technical facts.

Visualizing the Wisdom Ladder

graph TD
    A["Raw Base Model"] --> B["Stage 1: Persona & Etiquette (SFT)"]
    B --> C["Stage 2: Technical Mastery (SFT + Context)"]
    C --> D["Stage 3: Safety & Policy (DPO)"]
    
    subgraph "The Evolution of Intelligence"
    B
    C
    D
    end
    
    D --> E["The Perfect TechFlow Agent"]

3. Stage 3: The "Policy & Safety" Pass (DPO)

Goal: Ensure the model follows rules (e.g., "don't give refunds").
Data: 200 Pairs of "Chosen" vs "Rejected" answers (Module 12).
- Chosen: "I can't issue a refund, but I can credit your account for next month."
- Rejected: "Sure, I'll refund you right now."
Result: The model is now an "Aligned" employee that protects the company's bottom line.

4. Why we save checkpoints between stages

After every stage, you must run your Comparative Evaluation Set (Lesson 2).

If Stage 2 makes the model $20%$ more accurate but $50%$ less polite, you might decide to lower the Learning Rate for Stage 2 or add more "Etiquette" data into the second pass.

This is the "Cooking" phase of AI. You are constantly tasting the results and adjusting the spices.

Summary and Key Takeaways

Layered Training: Don't try to teach everything at once.
Persona First: Get the voice right before you move to the hard technical facts.
DPO for Policies: Use preference optimization (Module 12) to enforce strict business rules at the final stage.
Checkpointing: Always save the model after each stage so you can "Revert" if the next layer of training causes catastrophic forgetting.

In the next lesson, we will look at the hardest part of support: Handling Conflict and De-escalation.

Reflection Exercise

If you skip Stage 1 and go straight to Stage 2, why might the model struggle in production? (Hint: Does a user prefer a 'Smart Robot' or a 'Smart Colleague'?)
Why is "Learning Rate" usually lower in Stage 3 than in Stage 1? (Hint: See 'Weight Distributions' in Module 11).

SEO Metadata & Keywords

Focus Keywords: iterative fine-tuning process, multi-stage AI training, SFT followed by DPO, training technical expert AI, LLM persona vs accuracy. Meta Description: Case Study Part 3. Master the art of the Wisdom Ladder. Learn how to iteratively layer personality, technical expertise, and policy alignment to build the ultimate support agent.

Iterative Fine-Tuning: From 'Friendly' to 'Technical Expert'