skills.rest
DonNigami
DonNigamiCOMMUNITY
Explore all skills by this author →
GitHub

prompt-engineer-llm

Design prompts and RAG pipelines for production-grade LLM workflows.

Updated Jun 15, 2025
Search Tags:#production#fine-tuning#LLM#RAG#retrieval#prompt-engineering#knowledge-base
One-click install
npx skills add https://github.com/DonNigami/eddication.io --skill prompt-engineer-llm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineer-llm
Source: https://github.com/DonNigami/eddication.io/tree/main/.claude/skills/prompt-engineer-llm
Command: npx skills add https://github.com/DonNigami/eddication.io --skill prompt-engineer-llm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams design high-quality prompts, build retrieval-augmented generation (RAG) pipelines, and optimize AI workflows for production-grade LLMs.

Core Features & Use Cases

  • Prompt design and templates for zero-shot, few-shot, and Chain-of-Thought strategies.
  • RAG integration patterns with vector databases and retrieval pipelines.
  • LLM fine-tuning and workflow optimization for scalable AI systems.
  • Use Case: Build a customer-support assistant that answers policy questions by pulling relevant knowledge from a KB and responding concisely.

Quick Start

Use the prompt-engineer-llm skill to design a zero-shot prompt for a customer-support chatbot that uses retrieval-augmented generation to fetch policy details from the knowledge base and respond concisely.

Frequently Asked Questions about prompt-engineer-llm

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

FAQPage Schema
How do I design a prompt for a customer support chatbot using retrieval-augmented generation?▼

Prompt engineering strategies include zero-shot, few-shot, and Chain-of-Thought templates. These approaches structure LLM inputs to handle complex reasoning tasks, build AI agents, and optimize document processing pipelines for production environments.

What is the best way to integrate a vector database with an LLM knowledge base?▼

The best way to integrate a vector database with an LLM knowledge base is by implementing RAG integration patterns. These retrieval pipelines fetch relevant knowledge to ground LLM responses, ensuring accurate answers for enterprise AI workflows.

Do I need fine-tuning to build scalable AI systems with LLMs?▼

You do not always need fine-tuning to build scalable AI systems. Effective prompt engineering and RAG integration patterns often optimize LLM workflows sufficiently, though fine-tuning remains available for specific production-grade deployment requirements.

What are the limitations of using zero-shot prompts in enterprise LLM deployments?▼

Zero-shot prompts in enterprise LLM deployments may lack context specificity without a retrieval pipeline. Limitations include hallucination risks on internal policies, requiring RAG integration with a knowledge base to ensure safe, accurate enterprise responses.

Related Skills

Explore More Software Engineering Skills→
xurenluxurenlu
1

rag-implementation

Develop RAG systems integrating vector databases, embedding models, and retrieval strategies.

Community
Advanced
DriveConnect-alphaDriveConnect-alpha
1

ai-engineer

Develop production-grade LLM applications with RAG pipelines and agent orchestration.

Official
Advanced
HermeticOrmusHermeticOrmus
89

rag-implementation

Implement RAG systems integrating vector databases and semantic search for LLM applications.

Community
Advanced
SriRamkunamsettySriRamkunamsetty

obliteratus

Remove refusal behaviors from open-weight LLMs via the obliteratus CLI.

Community
Advanced