Lesson 12: Prompt Engineering Mastery: Few-Shot Learning
[A] Today’s Build
We’re implementing a production-grade few-shot learning system that dramatically improves LLM classification accuracy through strategic example selection:
FewShotEngine: Dynamically selects and injects optimal training examples into prompts
ExampleStore: Manages example libraries with embedding-based similarity search
ClassificationAgent: Extends L11’s CoT agent with few-shot capabilities
Performance Dashboard: Real-time accuracy tracking across example counts (0-shot, 1-shot, 3-shot, 5-shot)
Benchmark Suite: Automated testing framework measuring accuracy improvements
Building on L11:
We extend the CoT reasoning agent with example-guided learning, combining explicit reasoning traces with pattern recognition from demonstrations. L11’s state management and evaluation framework provide the foundation.
Enabling L13: Our token counting infrastructure and example selection logic directly prepare for context window optimization and dynamic prompt compaction.
Component Architecture: FewShotEngine manages the example lifecycle—storage, retrieval, injection. ExampleStore wraps a simple JSON-based vector store (upgradeable to Pinecone/Weaviate). ClassificationAgent extends L11’s SimpleAgent, adding few-shot capabilities while preserving CoT reasoning.
