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prompt-engineering-patterns

Identify and apply prompt-engineering patterns for reliable LLM outputs.

Updated Aug 23, 2026
Search Tags:#LLM#few-shot#prompt-engineering#system-prompts#structured-outputs#template-systems
One-click install
npx skills add https://github.com/brunoreinstein-cloud/chat-assitjur --skill prompt-engineering-patterns-brunoreinstein-cloud
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/brunoreinstein-cloud/chat-assitjur/tree/main/.agents/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/brunoreinstein-cloud/chat-assitjur --skill prompt-engineering-patterns-brunoreinstein-cloud

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. This Skill guides you in designing, testing, and deploying effective prompts, templates, and system prompts to ensure consistent and high-quality AI behavior.

Core Features & Use Cases

  • Few-Shot Learning: Build dynamic example retrieval and selection strategies to improve task performance with minimal examples.
  • Chain-of-Thought Prompting: Elicit structured reasoning and self-consistency to improve accuracy on complex tasks.
  • Structured Outputs: Enforce JSON/Pydantic-style schemas for reliable parsing and downstream use.
  • Template Systems: Create modular prompt components and reusable templates for multi-turn workflows.
  • System Prompt Design: Define roles, constraints, and behavior to guide model responses.
  • Integration & Evaluation: Patterns for RAG integration, tooling hooks, and prompt testing/evaluation.

Quick Start

Select a suitable prompt pattern from this skill and apply it to your task to produce a structured, reliable response.

Frequently Asked Questions about prompt-engineering-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I enforce structured outputs from LLMs for reliable JSON parsing?▼

To enforce structured outputs from LLMs, you apply JSON or Pydantic-style schemas within your prompt patterns. This ensures the model generates predictable, parseable responses for reliable downstream use.

What is the best way to design system prompts for consistent LLM behavior?▼

Designing system prompts involves defining specific roles, constraints, and behavioral guidelines to guide LLM responses. This pattern ensures consistent, high-quality AI behavior across production workflows.

How do I use few-shot prompting to improve LLM task performance?▼

Few-shot prompting improves LLM task performance by building dynamic example retrieval and selection strategies. This provides the model with minimal contextual examples to enhance accuracy.

Can I integrate prompt engineering patterns with RAG pipelines?▼

Yes, prompt engineering patterns integrate with RAG pipelines and evaluation tooling. They provide specific hooks for retrieval-augmented generation and prompt testing workflows.

When do I need chain-of-thought prompting for LLMs?▼

You need chain-of-thought prompting when solving complex tasks requiring structured reasoning. This pattern elicits step-by-step logical progression and self-consistency to maximize LLM accuracy.

How do I build modular template systems for multi-turn LLM workflows?▼

Building template systems for LLMs involves creating modular prompt components and reusable templates. This approach manages multi-turn workflows and ensures scalable prompt deployment.

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