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.

---

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=50):

words = text.split()

chunks = []

for i in range(0, len(words), chunk_size - overlap):

chunk = " ".join(words[i:i + chunk_size])

if chunk: chunks.append(chunk)

return chunks

def index_document(doc_id, content_text, metadata=None):

chunks = chunk_text(content_text)

ids = [f"{doc_id}_chunk_{i}" for i in range(len(chunks))]

collection.upsert(ids=ids, documents=chunks)

return len(chunks)

Pipeline De Query Com Rag

def rag_query(query, top_k=5, system=None):

results = collection.query(

query_texts=[query], n_results=top_k,

include=["documents", "metadatas", "distances"])

context_parts = []

for doc, meta, dist in zip(results["documents"][0],

results["metadatas"][0],

results["distances"][0]):

if dist < 1.5:

src = meta.get("source", "doc")

context_parts.append(f"[Fonte: {src}]

{doc}")

context = "

---

".join(context_parts)

response = client.messages.create(

model="claude-opus-4-20250805", max_tokens=1024,

system=system or "Responda baseado no contexto.",

messages=[{"role": "user", "content": f"Contexto:

{context}

{query}"}])

return response.content[0].text

---

Escolha Do Vector Db

DB	Melhor Para	Hosting	Custo
Chroma	Desenvolvimento, local	Self-hosted	Gratis
pgvector	Ja usa PostgreSQL	Self/Cloud	Gratis
Pinecone	Producao gerenciada	Cloud	USD 70+/mes
Weaviate	Multi-modal	Self/Cloud	Gratis+
Qdrant	Alta performance	Self/Cloud	Gratis+

Pgvector

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE knowledge_embeddings (

id UUID PRIMARY KEY DEFAULT gen_random_uuid(),

content TEXT NOT NULL,

embedding vector(1536),

metadata JSONB,

created_at TIMESTAMPTZ DEFAULT NOW()

);

CREATE INDEX ON knowledge_embeddings

USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);

SELECT content, 1 - (embedding <=> QUERY_VECTOR) AS similarity

FROM knowledge_embeddings ORDER BY similarity DESC LIMIT 5;

---

Estrutura De Prompt De Elite

Componentes do system prompt Auri:

• Identidade: Nome (Auri), Tom (Natural, caloroso, direto), Plataforma (Amazon Alexa)
• Regras: Maximo 3 paragrafos curtos, sem markdown, linguagem conversacional
• Capacidades: analise de negocios, conselho baseado em dados, criatividade
• Limitacoes: sem internet tempo real, sem transacoes financeiras
• Personalizacao: {user_name}, {user_preferences}, {relevant_history}

Chain-Of-Thought

def cot_analysis(problem: str) -> str:

steps = [

"1. O que exatamente esta sendo pedido?",

"2. Que informacoes sao criticas para resolver?",

"3. Quais abordagens possiveis existem?",

"4. Qual abordagem e melhor e por que?",

"5. Quais riscos ou limitacoes existem?",

]

prompt = f"Analise passo a passo:

PROBLEMA: {problem}

"

prompt += "

".join(steps) + "

Resposta final (concisa, para voz):"

return call_claude(prompt)

---

Cache Semantico

class SemanticCache:

def __init__(self, similarity_threshold=0.95):

self.threshold = similarity_threshold

self.cache = {}

def get_cached(self, query, embedding):

for cached_emb, (response, _) in self.cache.items():

if cosine_similarity(embedding, cached_emb) >= self.threshold:

return response

return None

def set_cache(self, query, embedding, response):

self.cache[tuple(embedding)] = (response, query)

Estimativa De Custos Claude

PRICING = {

"claude-opus-4-20250805": {"input": 15.00, "output": 75.00},

"claude-sonnet-4-5": {"input": 3.00, "output": 15.00},

"claude-haiku-3-5": {"input": 0.80, "output": 4.00},

}

def estimate_monthly_cost(model, avg_input, avg_output, req_per_day):

p = PRICING[model]

daily = (avg_input + avg_output) * req_per_day / 1e6

monthly = daily p["input"] 30

return {"model": model, "monthly_cost": "USD %.2f" % monthly}

---

Framework De Avaliacao

from anthropic import Anthropic

client = Anthropic()

def evaluate_response(question, expected, actual, criteria):

criteria_text = "

".join(f"- {c}" for c in criteria)

eval_prompt = (

f"Avalie a resposta do assistente de IA.

"

f"PERGUNTA: {question}

RESPOSTA ESPERADA: {expected}

"

f"RESPOSTA ATUAL: {actual}

Criterios:

{criteria_text}

"

"Nota 0-10 e justificativa para cada criterio. Formato JSON."

)

response = client.messages.create(

model="claude-haiku-3-5", max_tokens=1024,

messages=[{"role": "user", "content": eval_prompt}]

)

import json

return json.loads(response.content[0].text)

AURI_EVALS = [

{

"question": "Quais sao os principais riscos de abrir startup agora?",

"criteria": ["precisao_factual", "relevancia", "clareza_para_voz"]

},

]

---

6. Comandos

Comando	Acao
/rag-setup	Configura pipeline RAG completo
/embed-docs	Indexa documentos no vector DB
/prompt-optimize	Otimiza prompt para qualidade e custo
/cost-estimate	Estima custo mensal do LLM
/eval-run	Roda suite de evals de qualidade
/cache-setup	Configura cache semantico
/model-select	Escolhe modelo ideal para o caso de uso

Best Practices

• Provide clear, specific context about your project and requirements
• Review all suggestions before applying them to production code
• Combine with other complementary skills for comprehensive analysis

Common Pitfalls

• Using this skill for tasks outside its domain expertise
• Applying recommendations without understanding your specific context
• Not providing enough project context for accurate analysis