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llm-ops

Orchestrate end-to-end LLM operations for production deployments.

Updated Aug 23, 2026
Search Tags:#embeddings#llm#rag#evaluation#fine-tuning#prompt-engineering#vector-databases
One-click install
npx skills add https://github.com/ProgramadorBrasil/antigravity-skills --skill llm-ops
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-ops
Source: https://github.com/ProgramadorBrasil/antigravity-skills/tree/main/skills/llm-ops
Command: npx skills add https://github.com/ProgramadorBrasil/antigravity-skills --skill llm-ops

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LLM-Ops provides a standardized, production-grade framework to orchestrate data ingestion, model interactions, and deployment for reliable AI applications.

Core Features & Use Cases

  • RAG pipelines with embeddings and vector stores (Chroma, Pinecone, etc.) for retrieval-augmented generation.
  • Cost-aware model selection and prompt optimization to reduce runtime and expense.
  • End-to-end orchestration including indexing, retrieval, evaluation, and deployment workflows.
  • Baseline QA and evaluation frameworks.

Quick Start

Set up a RAG pipeline with embeddings and a vector store to answer domain-specific questions.

Frequently Asked Questions about llm-ops

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

FAQPage Schema
How do I build a production-grade RAG pipeline with embeddings and vector databases?▼

To build a production-grade RAG pipeline, you orchestrate data ingestion, generate embeddings, index them in vector databases, and manage retrieval workflows. This ensures reliable retrieval-augmented generation for domain-specific querying.

What is LLM Ops and when do I need it for AI deployments?▼

LLM Ops is the end-to-end orchestration of model interactions, data integration, and deployment. You need LLM Ops when transitioning AI systems to production to ensure reproducibility, scalability, and maintainability across serving workflows.

How can I optimize prompt engineering and model selection to reduce LLM costs?▼

You optimize LLM costs through cost-aware model selection and prompt engineering. Evaluating runtime and expense during the orchestration phase reduces overall deployment costs while maintaining AI system performance.

How do I set up evaluation and QA frameworks for fine-tuning workflows?▼

You set up QA frameworks by establishing baseline evaluations during fine-tuning and indexing workflows. This approach ensures model reliability and maintainability before deploying the AI system to production environments.

Does this LLM Ops workflow support reproducible and scalable AI systems?▼

Yes, the LLM Ops workflow supports reproducible and scalable AI systems. It standardizes data ingestion and deployment orchestration to satisfy strict production requirements for reliable, cost-aware operations.

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