prompt-engineering-patterns

Chain-of-thought prompting improves LLM reliability by enforcing structured reasoning patterns. This skill applies these patterns alongside self-consistency and verification steps to reduce errors and inconsistencies in multi-step tasks.

The best way to structure prompt templates for RAG pipelines is using modular system prompts. This skill provides an extensible framework with templates and validation flows to integrate seamlessly with RAG architectures.

You set up evaluation metrics for prompt engineering by applying A/B testing and performance metrics. This skill provides evaluation pipelines and prompt variation generation to iteratively enhance and measure LLM outputs.

This prompt engineering framework requires numpy as its only dependency. It provides scripts, references, and assets to apply structured reasoning patterns and guardrails without needing complex external libraries.

LLM outputs for coding tasks are inconsistent due to lacking verification steps. This skill enforces error recovery, self-consistency, and modular templates to guardrail against hallucinations and improve controllability.