skills.rest
NovusAevum
NovusAevumCOMMUNITY
Explore all skills by this author →
GitHub

prompt-engineering-patterns

Design production-ready prompts for LLM applications with few-shot and chain-of-thought techniques.

1|Updated Nov 10, 2025
Search Tags:#prompt engineering#prompt optimization#chain-of-thought#few-shot learning#AI engineering#system prompts#LLM development
One-click install
npx skills add https://github.com/NovusAevum/dev-agents-orchestration --skill prompt-engineering-patterns-novusaevum
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/NovusAevum/dev-agents-orchestration/tree/main/configs/claude-code/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/NovusAevum/dev-agents-orchestration --skill prompt-engineering-patterns-novusaevum

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, scikit-learn, sentence-transformers, openai, aiodataloader, and includes assets (resource) and references (resource) and scripts (resource) components.

What problem does it solve?

This Skill equips users with advanced techniques to design, optimize, and manage prompts for Large Language Models (LLMs), ensuring reliable, high-quality, and controllable outputs in production environments. It helps overcome challenges like inconsistent responses, hallucinations, and inefficient token usage.

Core Features & Use Cases

  • Few-Shot Learning: Strategies for selecting effective examples to guide LLM behavior, including semantic similarity, diversity sampling, and token-aware truncation.
  • Chain-of-Thought Prompting: Techniques to elicit step-by-step reasoning from LLMs, dramatically improving accuracy on complex tasks like math, logic, and code debugging.
  • Prompt Optimization & A/B Testing: Workflows for iteratively refining prompts, measuring performance metrics (accuracy, latency, token usage), and conducting A/B tests to find the most effective variations.
  • System Prompt Design: Guidance on structuring system prompts to define LLM roles, expertise, constraints, and desired output formats for specialized AI assistants.
  • Use Case: When building an LLM-powered customer support chatbot, use this skill to design robust system prompts, implement few-shot examples for common queries, and apply chain-of-thought for complex troubleshooting, ensuring consistent and accurate responses.

Quick Start

Optimize a prompt for sentiment analysis. Provide examples of positive, negative, and neutral sentiments, and suggest a chain-of-thought approach.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
How do I design prompts that reduce hallucinations and inconsistent LLM responses?▼

Prompt engineering techniques like chain-of-thought reasoning, system prompt structuring, and few-shot learning with semantic similarity selection minimize hallucinations by guiding LLMs through explicit reasoning steps and providing high-quality examples that constrain output behavior.

What's the best way to optimize prompts for production LLM applications?▼

Implement iterative prompt optimization through A/B testing, measure performance metrics like accuracy and token usage, apply few-shot selection strategies, and integrate chain-of-thought reasoning to systematically improve reliability and efficiency before deployment.

How do I implement few-shot learning to guide LLM behavior?▼

Few-shot learning selects effective examples using semantic similarity and diversity sampling to guide LLM responses. This Skill provides token-aware truncation and selection strategies to maximize impact while controlling input length.

Can I use system prompts to control LLM output format and role definition?▼

System prompts define LLM roles, expertise constraints, and desired output formats. This Skill covers structured system prompt design to create specialized AI assistants with consistent, controllable behavior across production workflows.

Why does chain-of-thought prompting improve accuracy on complex reasoning tasks?▼

Chain-of-thought prompting elicits step-by-step reasoning from LLMs, dramatically improving performance on math, logic, and code tasks by decomposing complex problems and forcing explicit intermediate reasoning rather than direct answers.

How do I integrate prompt engineering with RAG workflows and validation?▼

Combine prompt optimization with structured reasoning, error handling, and performance monitoring to build RAG-integrated systems. This Skill covers template systems and validation techniques that ensure reliable retrieval-augmented generation pipelines.

Related Skills

Explore More Software Engineering Skills→
ricablericable

sentence-transformers

Generate normalized text and sentence embeddings for semantic similarity workflows.

Community
Intermediate
adiytharpansaadiytharpansa

performance-monitor

Monitors system performance metrics and detects bottlenecks to guide optimization efforts for OpenClaw operations.

Community
Intermediate
shreed27shreed27

embeddings

Generate vector embeddings and perform semantic search against collections.

Community
Intermediate
monaccodemonaccode
30

prompt-engineer

Design and optimize LLM prompts with few-shot examples and output formatting.

Official
Advanced