Fine-Tuning Your AI Experts
Fine-tuning is the process of training your AI expert on specific examples to make it behave exactly as you want. Unlike prompt engineering which guides behavior through instructions, fine-tuning actually modifies the model’s weights to embed your desired behaviors directly.What is Fine-Tuning?
Fine-tuning takes a pre-trained language model and continues training it on your specific dataset, teaching it to respond in ways that match your exact requirements.Behavior Training
Teach your expert how to respond in specific situations
Knowledge Enhancement
Add domain-specific knowledge and terminology
Style Adaptation
Match your brand voice and communication style
Performance Improvement
Better accuracy for your specific use cases
Fine-Tuning vs Prompt Engineering
When to use Prompt Engineering:- Quick prototyping
- Testing different approaches
- Frequently changing requirements
- Simple behavioral adjustments
- Instant results needed
- Production deployments
- Consistent brand voice needed
- Complex domain knowledge
- Cost optimization (smaller fine-tuned models can replace larger ones)
- High-volume usage
- Speed: Prompt engineering is instant, fine-tuning takes time to train
- Cost: Prompts have no training cost, fine-tuning has upfront training cost but lower inference cost
- Flexibility: Prompts are easy to change, fine-tuning requires retraining
- Consistency: Prompts are variable, fine-tuning is highly consistent
- Data Required: Prompts need none, fine-tuning needs 50-500+ examples
The Fine-Tuning Process in B-Bot Hub
1. Data Collection Phase
Fine-tuning starts with gathering quality training examples:1
Chat with Your Expert
Use your expert naturally, having real conversations
2
Mark Quality Interactions
When you get a good response, use QA Marking to save it
3
Capture Both Sides
Each marked interaction includes:
- User question/input
- Expert’s ideal response
4
Build Your Dataset
Continue marking until you have 50-500+ examples
- Click the three dots on the message
- Select “Start QA Marking”
- The interaction is saved to your training dataset
- Continue marking good examples
Quality over Quantity: 100 high-quality examples beat 1,000 mediocre ones
2. Training Phase
Once you have enough examples:1
Access Training
Navigate to Training in the expert menu
2
Review Dataset
See all your marked QA pairs
- Edit or remove bad examples
- Ensure diversity of situations
- Check for consistent quality
3
Configure Training
Set training parameters:
- Base model to fine-tune
- Number of epochs
- Learning rate (advanced)
4
Start Training
Click Start Training Job
- Training takes 10-60 minutes
- You’ll be notified when complete
3. Evaluation Phase
After training completes:- View all model versions (original + fine-tuned versions)
- A/B test by switching between versions in chat
- Compare responses to same questions
- Set the best version as primary
- Continue iterative improvement
Training Data Best Practices
Quality Guidelines
Diverse Examples
Diverse Examples
Cover different scenarios:
- Various question types
- Different tones (formal, casual)
- Edge cases and unusual requests
- Common vs rare situations
Consistent Style
Consistent Style
Maintain uniform behavior:
- Same tone across examples
- Consistent formatting
- Similar response length patterns
- Unified brand voice
Clear Examples
Clear Examples
High-quality pairs:
- Clear user intent
- Excellent expert response
- No ambiguity
- Complete information
Representative Data
Representative Data
Match real usage:
- Actual user questions
- Real-world scenarios
- Common pain points
- Typical interactions
How Many Examples?
Minimum (50-100 examples):- Basic behavior adjustments
- Simple use cases
- Style consistency
- Initial experiments
- Production deployments
- Complex behaviors
- Domain expertise
- Robust performance
- Specialized domains
- High-stakes applications
- Maximum performance
- Complex reasoning
Common Use Cases
1. Brand Voice Training
Ensure consistent brand communication across all interactions by training on examples that match your brand’s specific tone, terminology, and style.2. Domain Expertise
Create true specialists in medical, legal, technical, or other domains by training on domain-specific terminology, procedures, and best practices.3. Customer Support
Handle support tickets efficiently with training on common issues, escalation protocols, troubleshooting steps, and professional empathy.4. Sales & Marketing
Develop expert sales assistants trained on product details, feature comparisons, pricing discussions, and lead qualification approaches.Cost Optimization
Fine-tuning enables significant cost savings by allowing smaller models to perform as well as larger ones: Before Fine-Tuning:- Large Model (GPT-4): $0.03 per 1K tokens
- Monthly cost (1M tokens): $30,000
- Fine-tuned Smaller Model (GPT-3.5): $0.002 per 1K tokens
- Monthly cost (1M tokens): $2,000
- Savings: $28,000/month (93% reduction!)
Best Practices Summary
Start Small
Begin with 50-100 examples, iterate from there
Quality First
Perfect examples > many mediocre ones
Test Thoroughly
A/B test all versions before production
Document Everything
Track what changes between versions
Monitor Continuously
Watch metrics after each deployment
Iterate Often
Small frequent improvements beat big rare ones
Troubleshooting
Model Not Improving
Model Not Improving
Possible causes:
- Insufficient training data
- Low quality examples
- Conflicting examples
- Wrong base model
- Add more diverse examples
- Review and clean dataset
- Ensure consistency
- Try different base model
Model Overfitting
Model Overfitting
Symptoms:
- Perfect on training data
- Poor on new questions
- Too rigid responses
- Add more diverse examples
- Reduce training epochs
- Use regularization
- Expand dataset variety
Inconsistent Results
Inconsistent Results
Causes:
- Conflicting examples
- Ambiguous training data
- Too few examples
- Review dataset for conflicts
- Remove ambiguous examples
- Add clearer examples
- Ensure consistency