ai-engineer

Automate design and orchestration of LLM applications, RAG systems, and agents.

Updated Apr 12, 2026
One-click install
npx skills add https://github.com/BoraPerusic/agents --skill ai-engineer-boraperusic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-engineer
Source: https://github.com/BoraPerusic/agents/tree/main/skills/to%20try/ai-engineer
Command: npx skills add https://github.com/BoraPerusic/agents --skill ai-engineer-boraperusic

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Build production-grade LLM applications, RAG systems, and intelligent agent architectures, bringing production readiness, reliability, and observability to AI projects.

Core Features & Use Cases

  • Production-grade architecture design, model management, and observability for LLM apps.
  • Advanced RAG systems with multi-model orchestration and vector databases.
  • Agent frameworks and memory for multi-agent workflows.
  • Safety, monitoring, and cost controls for enterprise deployments.

Quick Start

Clarify use cases, constraints, and success metrics, then design the AI architecture and select models.

Frequently Asked Questions about ai-engineer

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

FAQPage Schema
How do I design a production-grade LLM application architecture?▼

Design production-grade LLM applications by clarifying use cases, constraints, and success metrics first. Then, define the AI architecture, select appropriate models, and establish observability to ensure reliability and scalable multi-model integration.

What is the best way to build an advanced RAG system with vector search?▼

Build advanced RAG systems by orchestrating multi-model integration alongside vector databases. This approach retrieves relevant context efficiently, ensuring production readiness and robust performance for enterprise AI deployments.

How do I manage multi-agent workflows and memory for intelligent agents?▼

Manage multi-agent workflows by utilizing agent frameworks equipped with memory capabilities. This enables intelligent agents to maintain context, orchestrate complex tasks, and execute scalable workflows reliably across enterprise environments.

How do I implement safety, monitoring, and cost controls for enterprise AI deployments?▼

Implement safety, monitoring, and cost controls by integrating observability frameworks into your LLM architecture. This tracks model performance, enforces safety guardrails, and manages operational expenses during enterprise AI deployments.

Can I use this approach for multi-model orchestration in scalable agent workflows?▼

Yes, you can use this approach for multi-model orchestration in scalable agent workflows. It supports integrating multiple models within a unified architecture, ensuring coordinated execution and reliable performance across diverse AI agents.