prompt-engineering-patterns

This skill provides a comprehensive framework for designing, optimizing, and maintaining production-grade prompt templates. It is intended for software engineers, AI developers, and technical product managers building complex LLM applications. The skill focuses on moving beyond basic chat interactions to robust, repeatable, and scalable prompt engineering patterns that ensure consistency across diverse use cases. By leveraging techniques such as chain-of-thought, few-shot learning, and structured template systems, users can significantly reduce hallucinations and improve output reliability in high-stakes environments.

• Few-Shot Learning: Strategies for effective example selection using semantic similarity and diversity sampling to maximize context window utility.

• Chain-of-Thought (CoT) Prompting: Techniques for eliciting step-by-step reasoning, including zero-shot CoT, reasoning traces, and self-consistency verification.

• Prompt Optimization: Workflows for iterative refinement, A/B testing variations, measuring performance metrics like accuracy and latency, and reducing token consumption.

• Modular Template Systems: Implementation of variable interpolation, role-based composition, and conditional logic for reusable, dynamic prompt components.

• System Prompt Design: Establishing behavioral constraints, defining output formats, and enforcing safety guidelines at the system layer.

• Integration Patterns: Best practices for combining prompts with RAG (Retrieval-Augmented Generation) systems, self-verification steps, and automated validation logic.

• Users should start with progressive disclosure, moving from simple instructions to complex reasoning chains only as required to solve the specific task.

• Always treat prompts as versioned code; maintain documentation for intent and track performance metrics to detect regressions over time.

• Be aware of context overflow; balance the quantity of few-shot examples with the available token window and target task complexity.

• Prioritize specific instructions and high-quality representative examples over lengthy, vague descriptions to minimize ambiguity and improve instruction following.

• Use the provided Python integration patterns to build programmatic workflows for prompt rendering, selection, and automated evaluation.