# Llmops

> Manages the lifecycle of large language models in production, covering model versioning, prompt management, inference optimization, and cost control.

- Skill: `neuralblitz/llmops` (Agent Skill)
- Install (CLI): `npx skillmds@latest add neuralblitz/llmops`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neuralblitz/llmops/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML, DevOps & Infra, Model Training & Fine-tuning, Monitoring & Observability
- Tags: Cost Management, Inference Optimization, Langsmith, Llmops, Mlflow, Model Versioning, Prompt Management, Weights And Biases
- Author: NeuralBlitz (https://skillmd.com/u/neuralblitz)
- Updated: 2026-08-22
- Page: https://skillmd.com/skills/neuralblitz/llmops

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# LLMops

LLMOps is the practice of managing, deploying, and monitoring large language models in production. It encompasses the entire lifecycle from data prep to model serving.

## Key Concepts

- Model versioning
- Prompt management
- A/B testing for prompts
- Inference optimization
- Cost management

## Common Use Cases

- LLM API management
- Prompt engineering at scale
- Model fine-tuning pipelines
- Evaluation frameworks
- Production monitoring

## Best Practices

- Version prompts and configs
- Implement observability
- Use proper caching
- Set up rate limiting
- Monitor latency/cost

## Resources

- MLflow, LangSmith, Weights & Biases
- Related Skills: mlops, langchain, monitoring