prompt-engineering-patterns

Few-shot prompt selection improves LLM output accuracy by integrating intelligent example selection and chaining techniques. This Skill provides methods to construct prompt templates that systematically enhance model responses for complex reasoning tasks.

The best way to structure prompt templates for production workflows is using modular systems adaptable to multiple tasks. This Skill provides advanced construction methods incorporating structured outputs and multi-step reasoning to maximize LLM robustness.

Self-verification in prompt engineering handles error recovery by integrating feedback loops and fallback mechanisms. This approach improves robustness by validating model responses and systematically refining prompts to deploy reliable AI solutions.

Yes, you can use LangChain and Pydantic for structured LLM outputs. This Skill employs these dependencies alongside numpy and scikit-learn to construct advanced prompt systems, ensuring controllable and meaningful outputs across diverse applications.

Optimizing prompts for token efficiency and latency reduction involves adjusting prompt construction methods to balance output accuracy and performance. This Skill provides performance optimization strategies to refine prompts, ensuring reliable AI solutions in production.