AI Engineering & MLOps Guide

소개

This skill acts as a domain-specific expert for the entire AI lifecycle, drawing from industry-standard practices for building reliable machine learning systems. It assists users in making critical architectural decisions, such as choosing between RAG and fine-tuning, curating high-quality datasets, and implementing robust evaluation frameworks for open-ended LLM outputs. Whether you are optimizing inference latency for high-throughput systems or designing AI agents with sophisticated feedback loops, this skill provides the patterns and methodologies needed to successfully transition AI projects from prototype to production.

주요 기능

• Decision frameworks for RAG, fine-tuning, and prompt engineering
• Detailed evaluation methodologies for LLMs and AI agents
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• Inference optimization techniques for latency and cost reduction
• Strategies for dataset curation and synthetic data generation
• Architectural patterns for guardrails and user feedback loops

사용 사례

• Optimizing LLM serving infrastructure through quantization and batching strategies
• Architecting a production-ready RAG system with hybrid search and optimized chunking
• Developing automated evaluation pipelines to measure model accuracy and safety