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prompt-engineering-patterns

Optimize production prompts for few-shot learning, structured outputs, and verification flows.

Updated Dec 13, 2025
Search Tags:#chain-of-thought#few-shot#prompt-engineering#prompt-templates#structured-output#system-prompts
One-click install
npx skills add https://github.com/Posteriot/makalah-app --skill prompt-engineering-patterns-posteriot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/Posteriot/makalah-app/tree/main/.agents/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/Posteriot/makalah-app --skill prompt-engineering-patterns-posteriot

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill eliminates unpredictable LLM behavior by supplying production-tested prompt engineering patterns that balance few-shot learning, chain-of-thought reasoning, structured outputs, template systems, and optimization practices.

Core Features & Use Cases

  • Pattern Library: Detailed strategies for few-shot example selection, chain-of-thought, structured JSON enforcement, system prompts, and prompt templates to steer responses confidently.
  • Optimization Playbook: Guidance on iterative refinement, token efficiency, error recovery, and verification to keep prompts consistent even under edge cases.
  • Use Case: Build a customer support agent prompt pipeline that dynamically selects relevant examples, enforces JSON structure, and vets answers before returning them to users.

Quick Start

Optimize a SQL prompt by combining structured output models, chain-of-thought reasoning, and curated few-shot examples.

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 JSON outputs from an LLM in production?▼

You can enforce structured JSON outputs by applying prompt engineering patterns that constrain the model's response format, ensuring reliable parsing and integration in production template systems.

What is the best way to select few-shot examples for chain-of-thought reasoning?▼

Select few-shot examples dynamically by using prompt templates that match the input context, steering the LLM's chain-of-thought reasoning toward accurate and consistent outputs.

How do I build a customer support prompt pipeline that verifies LLM answers?▼

Build a customer support pipeline by combining system prompts, dynamic example selection, and reasoning verification flows to vet LLM answers before returning them to users.

How do I optimize LLM prompts for token efficiency and error recovery?▼

Optimize LLM prompts for token efficiency and error recovery by iteratively refining instructions and applying token-efficient caching strategies to handle edge cases consistently.

Can I use numpy with prompt templates for dynamic example selection?▼

Yes, you can use numpy to support dynamic example selection within prompt templates, leveraging numerical operations to programmatically retrieve and rank relevant few-shot examples.

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