What are prompt engineering patterns?

They are standardized techniques like few-shot learning and chain-of-thought used to improve the accuracy, reliability, and consistency of AI model outputs.

Does it support dynamic example selection?

Yes, it includes patterns for retrieving and selecting the most relevant examples for few-shot learning based on the user's specific input query.

How does this skill help with production LLM apps?

It provides templates and best practices for creating reliable, controllable prompts that handle edge cases and minimize errors in real-world environments.

Can this skill help reduce API costs?

Yes, it includes techniques for token efficiency and prompt optimization to reduce the amount of context processed by the model without sacrificing quality.

What is Chain-of-Thought (CoT) prompting?

CoT is a technique that encourages the model to break down complex problems into step-by-step reasoning paths before providing a final answer, improving performance on logic tasks.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: Cursedpotential

byCursedpotential

Ciencia de Datos y ML

Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning and chain-of-thought reasoning.

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Prompt Engineering Patterns provides a comprehensive framework for mastering advanced LLM interactions to maximize production-grade reliability and controllability. This skill empowers users to design complex prompt templates, implement structured reasoning patterns such as chain-of-thought, and utilize dynamic few-shot learning systems. By integrating best practices for system prompt design and iterative refinement, it helps developers build more consistent AI applications while reducing token overhead and latency, ensuring specialized AI assistants behave according to strict constraints.

Características Principales

Dynamic few-shot learning with semantic example selection
0 GitHub stars
Modular prompt template systems with variable interpolation
Structured Chain-of-Thought reasoning elicitation
System prompt optimization for specialized AI personas
Iterative prompt refinement and performance tracking

Casos de Uso

Reducing inconsistent outputs in specialized AI assistants
Optimizing token usage and latency for high-scale AI deployments
Designing production-ready prompts for complex LLM applications

Acerca de

Características Principales

Dynamic few-shot learning with semantic example selection
0 GitHub stars
Modular prompt template systems with variable interpolation
Structured Chain-of-Thought reasoning elicitation
System prompt optimization for specialized AI personas
Iterative prompt refinement and performance tracking

Casos de Uso

Reducing inconsistent outputs in specialized AI assistants
Optimizing token usage and latency for high-scale AI deployments
Designing production-ready prompts for complex LLM applications