Cross-Domain Agents
Design agents that transfer knowledge across different domains
Your Progress
0 / 5 completedBridging Domain Gaps
Domain adaptation is the process of fine-tuning a transferred model to excel in the target domain. The strategy you choose depends on your data availability, accuracy requirements, and budget constraints.
Interactive: Adaptation Strategy Selector
Compare different adaptation strategies:
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Supervised Fine-tuning
Standard approach: collect labeled data in target domain, fine-tune model
Requirements
Data Needed:1,000-10,000 labeled examples
Effort:High
Accuracy:Very High (85-95%)
Trade-offs
✓ Best accuracy, straightforward implementation
✗ Expensive labeling, time-consuming data collection
When to Use:
Mission-critical applications, sufficient budget
Interactive: Domain Gap Analyzer
Adjust the domain gap to see how it affects adaptation requirements:
Domain Similarity Gap
40%
Very SimilarVery Different
Difficulty
Moderate
Expected Accuracy
70%
Adaptation Time
17d
Data Needed
2100
⚠️ Moderate gap - consider domain adaptation techniques
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Adaptation Best Practices
- •Start Simple: Try few-shot first, scale up only if needed
- •Measure Domain Gap: Use embedding similarity to quantify source-target distance
- •Freeze Wisely: Freeze early layers (general features), fine-tune late layers (domain-specific)
- •Use Low Learning Rate: 10x lower than initial training to preserve transferred knowledge
- •Monitor Catastrophic Forgetting: Test on source domain periodically to ensure retained performance