Meta-Learning for Agents
Implement meta-learning for agents that adapt to new tasks quickly
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0 / 5 completedFew-Shot Learning in Practice
Few-shot learning means adapting to new tasks with K examples (typically 1-50). Meta-trained agents excel at this. Three common scenarios: 1-shot (single example), 3-shot (few examples), 5-shot (several examples). Performance improves with more shots, but even 1-shot outperforms standard training with 1000+ examples.
Interactive: Few-Shot Performance
See how accuracy improves with more examples:
Few-Shot Prompting Strategy
For LLM-based agents, few-shot learning happens through prompt engineering. Provide K examples in the prompt, model adapts behavior without parameter updates. Meta-learned agents need fewer examples for same performance.
# Few-Shot Prompt Template
system_prompt = """You are a customer support agent.
Here are examples of how to handle different situations:
Example 1:
Input: Customer angry about delayed delivery
Response: I sincerely apologize for the delay. Let me check
the status immediately and offer compensation.
Example 2:
Input: Customer wants refund for damaged item
Response: I'm sorry about the damaged item. I'll verify the
damage and process your refund right away.
Now handle this new case:"""
user_query = "Customer billing inquiry - duplicate charge"
response = llm.generate(system_prompt + user_query)Shot Selection Strategy
Not all examples are equal. Select diverse, representative examples that cover edge cases. Bad: 3 similar examples. Good: 3 examples covering different scenarios. Use clustering or diversity sampling to choose examples that maximize information. Well-selected 3-shot can outperform random 5-shot.