Can prompt engineering help reduce token usage?

Yes, the optimization patterns included focus on 'progressive disclosure' and token efficiency to minimize costs while maintaining output quality.

Does this skill work with LangChain and Anthropic SDK?

Yes, the skill provides implementation examples for LangChain, the Anthropic Python SDK, and general template-based approaches.

When should I use Chain-of-Thought prompting?

Use Chain-of-Thought (CoT) when the task requires multi-step logic, mathematical reasoning, or complex decision-making where the 'path' to the answer is as important as the answer itself.

How do I ensure Claude returns valid JSON?

This skill includes patterns for using Pydantic schemas and system-level instructions that force the model to adhere to specific structured data formats.

What are prompt engineering patterns?

Prompt engineering patterns are standardized techniques like chain-of-thought and few-shot learning used to improve the accuracy, reasoning, and reliability of LLM responses.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: gwickman

bygwickman

0•

Ciencia de Datos y ML

Implement industry-standard prompt engineering techniques to improve LLM accuracy, reliability, and structured output handling.

This skill provides a comprehensive library of advanced prompt engineering patterns designed to elevate LLM performance in production environments. From implementing robust chain-of-thought reasoning and few-shot learning to enforcing strict JSON schemas with Pydantic, it equips developers with the tools to build more controllable and consistent AI applications. It covers essential strategies for system prompt design, dynamic example selection, and error recovery, ensuring your Claude-powered applications are both efficient and resilient.

Características Principales

01Structured output enforcement using Pydantic and JSON mode

02Advanced system prompt patterns for specialized AI personas

030 GitHub stars

04Standardized Chain-of-Thought (CoT) implementations for complex reasoning

05Dynamic few-shot learning with semantic similarity example selection

06Template systems for modular, reusable prompt components

Casos de Uso

01Reducing hallucinations through multi-step verification and reasoning traces

02Optimizing LLM response consistency for production-grade applications

03Converting unstructured text into type-safe JSON data structures

Características Principales

01Structured output enforcement using Pydantic and JSON mode

02Advanced system prompt patterns for specialized AI personas

030 GitHub stars

04Standardized Chain-of-Thought (CoT) implementations for complex reasoning

05Dynamic few-shot learning with semantic similarity example selection

06Template systems for modular, reusable prompt components

Casos de Uso

01Reducing hallucinations through multi-step verification and reasoning traces

02Optimizing LLM response consistency for production-grade applications

03Converting unstructured text into type-safe JSON data structures