How do we implement few-shot learning in our workflow?
The magic of few-shot is in smart model prompts.
How It Works:
Use prompt-engineering or adapter layers on a base LLM: embed your examples into the input or fine-tune lightweight parameters on those samples.
Key Benefits:
- Seamless integration: No full retraining needed.
- Fast iteration: Update model behavior with new examples instantly.
- Scalable across tasks: Same infrastructure supports many scenarios.
Real-World Use Cases:
- Customer support: Teach new FAQ responses overnight.
- Document tagging: Adapt to new taxonomy with minimal effort.
FAQs
Which platforms support few-shot?
How monitor quality?
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