Model Training & Customization / Customization Methods

Fine-Tuning

Advanced [4/5]
Model fine-tuning Transfer learning Domain adaptation

Definition

Fine-tuning is the process of taking a pre-trained LLM and training it further on a specific dataset to specialize it for a particular task or domain. Unlike prompting, fine-tuning actually modifies the model's weights.

Think of it as teaching an already-educated person a new specialty—they keep their general knowledge but gain expertise in a specific area.

Key Concepts

  • Base model: The pre-trained model you start with
  • Training data: Examples specific to your use case
  • Weight updates: Fine-tuning modifies model parameters
  • Overfitting risk: Model may lose general capabilities

Examples

Fine-Tuning Data Format
Training Examples Structure
{ "messages": [ {"role": "system", "content": "You are a legal assistant"}, {"role": "user", "content": "What is a tort?"}, {"role": "assistant", "content": "A tort is a civil wrong..."} ] } { "messages": [ {"role": "system", "content": "You are a legal assistant"}, {"role": "user", "content": "Explain liability"}, {"role": "assistant", "content": "Liability refers to..."} ] } // Need 50-100+ high-quality examples minimum
Fine-tuning requires structured conversation examples showing the desired behavior.
Decision Framework
When to Fine-Tune vs Prompt
FINE-TUNING ✓ • Specific output format consistently needed • Specialized domain terminology • Unique writing style/tone • Latency critical (shorter prompts) • High volume, cost matters PROMPTING + RAG ✓ • Knowledge changes frequently • Need to cite sources • Quick iteration required • Limited training data • Flexibility needed HYBRID APPROACH • Fine-tune for style/format • Use RAG for knowledge
Choose based on whether you need to change how the model responds vs what it knows.
Fine-Tuning Process
Steps to Fine-Tune a Model
1. PREPARE DATA → Gather 100+ high-quality examples → Format as conversations (JSONL) → Split into train/validation sets 2. CHOOSE BASE MODEL → Consider size vs capability tradeoffs → Check licensing requirements 3. CONFIGURE TRAINING → Set learning rate (typically 1e-5) → Define epochs (1-3 usually enough) → Set batch size 4. TRAIN & MONITOR → Watch validation loss → Check for overfitting 5. EVALUATE & ITERATE → Test on held-out examples → Refine data if needed
Fine-tuning is an iterative process that requires careful monitoring.

Interactive Exercise

📈
Fine-Tuning Decision

A company wants to build a customer support bot that:

  • Answers questions about their 500+ products
  • Uses their specific brand voice
  • Product info updates weekly

Should they fine-tune, use RAG, or both? Explain your reasoning.

Pro Tips
  • Start with prompting—only fine-tune if prompting isn't enough
  • Quality of training data matters more than quantity
  • Fine-tuning changes behavior, RAG adds knowledge
  • Always maintain a validation set to detect overfitting

Related Terms

RAG LoRA Transfer Learning LLM