llm-ops

LLM-OPS -- IA de Producao

Overview

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao. Ativar para: implementar RAG, criar pipeline de embeddings, Pinecone/Chroma/pgvector, fine-tuning, prompt engineering, reducao de custos de LLM, evals, cache semantico, streaming, agents.

When to Use This Skill

• When you need specialized assistance with this domain

Do Not Use This Skill When

• The task is unrelated to llm ops
• A simpler, more specific tool can handle the request
• The user needs general-purpose assistance without domain expertise

How It Works

A diferenca entre um prototipo de IA e um produto de IA e operabilidade.

LLM-Ops e a engenharia que torna IA confiavel, escalavel e economica.

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Arquitetura Rag Completa

[Documentos] -> [Chunking] -> [Embeddings] -> [Vector DB]

[Query] -> [Embed query] -> [Semantic Search] -> [Top K chunks]

[LLM + Context] -> [Resposta]

Pipeline De Indexacao

from anthropic import Anthropic import chromadb

client = Anthropic() chroma = chromadb.PersistentClient(path="./chroma_db")

def chunk_text(text, chunk_size=500, overlap=