ai-llm

End-to-end LLM deployment spans strategy selection, fine-tuning with PEFT/LoRA, automated evaluation, and serving via quantized models (FP8/FP4). This Skill streamlines the full lifecycle from prompt design through production CI/CD, reducing time-to-value and ensuring robust, observable deployments.

Automated evaluation pipelines test LLM outputs against functional requirements and safety guardrails. This Skill provides governance frameworks, multi-layer guardrails, and monitoring to detect drift and ensure production-grade quality before deployment.

PEFT and LoRA fine-tuning reduce training cost while adapting models to specific tasks. This Skill guides dataset design, strategy selection between prompting and fine-tuning, and configuration for production deployment across diverse LLM systems.

Yes. This Skill covers RAG pipelines and agentic workflows as core lifecycle patterns, helping you design, evaluate, and deploy them with proper governance, quantization, and safety guardrails integrated into production CI/CD.

FP8 and FP4 quantization reduce model size and latency for serving. This Skill provides vLLM-based deployment configurations and quantization strategies to optimize production inference while maintaining evaluation and safety standards.

Drift detection and safety guardrails track model behavior shifts and policy violations post-deployment. This Skill integrates monitoring, multi-layer guardrails, and production CI/CD patterns to ensure ongoing operational reliability.