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vasilyu1983
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ai-llm-engineering

Automate design and deployment of production-grade AI agents.

73|16|Updated Nov 14, 2025
Search Tags:#deployment#AI agents#evaluation#fine-tuning#RAG#LLMOps#LLM engineering
One-click install
npx skills add https://github.com/vasilyu1983/AI-Agents-public --skill ai-llm-engineering
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-llm-engineering
Source: https://github.com/vasilyu1983/AI-Agents-public/tree/main/frameworks/claude-code-kit/initial-setup/skills/ai-llm-engineering
Command: npx skills add https://github.com/vasilyu1983/AI-Agents-public --skill ai-llm-engineering

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Deploying and managing LLM systems in production involves complex challenges in architecture, evaluation, deployment, and safety. This Skill provides operational patterns and best practices for building robust LLM applications.

Core Features & Use Cases

  • End-to-End LLM Lifecycle: Covers data preparation, fine-tuning (PEFT/LoRA), evaluation, deployment (vLLM), and LLMOps (monitoring, drift detection).
  • Advanced Architectures: Design RAG pipelines, agentic workflows (ReAct, multi-agent orchestration), and prompt engineering strategies for complex tasks.
  • Production-Ready Standards: Integrates modern advances like vLLM for 24x throughput, multi-layered security, and CI/CD-aligned evaluation for reliable systems.

Quick Start

Use the ai-llm-engineering skill to design a RAG pipeline for a customer support chatbot, including chunking and hybrid retrieval.

Frequently Asked Questions about ai-llm-engineering

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

FAQPage Schema
How do I build and deploy production-grade AI agents with RAG and tool integration?▼

This Skill automates design and deployment of production AI agents, including RAG pipelines, tool-using workflows, multi-agent orchestration, and safety guardrails. It provides end-to-end patterns from data preparation through evaluation and vLLM deployment with observability standards.

What's the best way to evaluate and fine-tune LLM systems before production?▼

The Skill covers the full LLM lifecycle: data preparation, fine-tuning via PEFT and LoRA, CI/CD-aligned evaluation, and monitoring with drift detection. It integrates modern standards for reliable, validated deployments at scale.

Can I use this for multi-agent systems and agent handoff orchestration?▼

Yes. The Skill supports agent-to-agent protocols, handoff orchestration, action loops, and multi-agent systems. It includes MCP integration for tool access, memory systems, and multi-layer security gates.

How do I design a RAG pipeline with chunking and hybrid retrieval?▼

The Skill provides templates and best practices for RAG architecture, including chunking strategies, hybrid retrieval design, and integration with agentic workflows. Quick-start guidance covers building customer support chatbots and similar use cases.

What observability and safety standards does this support?▼

The Skill implements OpenTelemetry observability standards and multi-layered security guardrails. It satisfies requirements for monitoring, drift detection, and validation logic across distributed agent systems.

Does this work with vLLM for high-throughput deployment?▼

Yes. The Skill integrates vLLM deployment for 24x throughput improvement and includes LLMOps patterns for production monitoring, scaling, and CI/CD-aligned evaluation workflows.

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