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llmops-operations

Manages the end-to-end lifecycle of LLM applications, including prompt engineering, RAG, evaluation, and deployment.

Updated Dec 23, 2025
Search Tags:#deployment#prompt engineering#optimization#rag#evaluation#llmops
One-click install
npx skills add https://github.com/take566/skills --skill llmops-operations
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llmops-operations
Source: https://github.com/take566/skills/tree/main/llmops
Command: npx skills add https://github.com/take566/skills --skill llmops-operations

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the entire lifecycle of LLM applications, from initial design and prompt engineering to deployment, evaluation, and cost optimization.

Core Features & Use Cases

  • Prompt Management: Version control and optimize prompts for better performance.
  • RAG Implementation: Build and manage Retrieval-Augmented Generation systems.
  • Evaluation Pipelines: Set up automated testing and performance monitoring.
  • Cost Optimization: Monitor and reduce token usage in LLM applications.
  • Use Case: Deploying a new customer support chatbot? Use this Skill to design effective prompts, integrate a knowledge base via RAG, set up an evaluation pipeline to ensure accuracy, and monitor its operational costs.

Quick Start

Use the llmops-operations skill to build a basic RAG system by providing your documents and a query.

Frequently Asked Questions about llmops-operations

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

FAQPage Schema
How do I build a RAG system for an LLM application?▼

Build a basic RAG system by providing your documents and a query to the skill. This process integrates a knowledge base with your LLM to ground responses in your specific data.

What is the best way to manage and optimize prompts for production?▼

Prompt management involves version control and continuous optimization to improve LLM performance. This ensures your prompts remain effective and consistent throughout the application lifecycle.

How do I set up evaluation pipelines for LLM applications?▼

Set up evaluation pipelines by configuring automated testing and performance monitoring for your LLM applications. This ensures accuracy and tracks operational costs throughout the application lifecycle.

Can I monitor and reduce token usage in my LLM application?▼

Yes, you can monitor and reduce token usage through cost optimization features. This functionality tracks token consumption to help lower the operational expenses of your LLM applications.

Does LLM application deployment support fine-tuning workflows?▼

Yes, LLM application deployment supports fine-tuning workflows alongside prompt engineering and RAG implementation. This manages the end-to-end lifecycle from initial design to production deployment.

When do I need LLMOps for my AI operations?▼

You need LLMOps when deploying production LLM applications like customer support chatbots. It handles design, knowledge base integration, evaluation, and cost monitoring for operational AI systems.

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