Visão Geral

Este curso aborda técnicas avançadas para otimização de desempenho, escalabilidade, custo e qualidade em arquiteturas Retrieval-Augmented Generation (RAG). O participante aprenderá a identificar gargalos em pipelines de recuperação e geração, otimizar bancos vetoriais, aprimorar mecanismos de busca, reduzir latência, aumentar throughput e melhorar a precisão das respostas geradas por aplicações baseadas em Large Language Models (LLMs). O curso explora estratégias utilizadas em ambientes corporativos para garantir alta performance, eficiência operacional e excelente experiência do usuário.

Objetivo

Após realizar este curso, você será capaz de:

• Identificar gargalos em arquiteturas RAG
• Otimizar processos de recuperação, reranking e geração
• Melhorar desempenho de bancos vetoriais e mecanismos de busca
• Reduzir latência e custos operacionais
• Implementar observabilidade e monitoramento de performance
• Construir soluções RAG escaláveis para ambientes corporativos

Publico Alvo

• Engenheiros de IA e Machine Learning
• Engenheiros LLMOps e MLOps
• Arquitetos de Soluções
• Desenvolvedores de aplicações baseadas em LLMs
• Profissionais de Platform Engineering
• Especialistas em desempenho e observabilidade

Pre-Requisitos

• Conhecimentos equivalentes ao curso RAG Fundamentals
• Familiaridade com Large Language Models
• Conhecimentos básicos de embeddings e bancos vetoriais
• Experiência com aplicações de IA Generativa é recomendada

Conteúdo Programatico

Module 1: Introduction to RAG Performance Optimization

1. Performance challenges in RAG systems
2. End-to-end RAG architecture analysis
3. Performance metrics and KPIs
4. Cost versus performance trade-offs
5. Enterprise scalability requirements
6. Optimization lifecycle overview

Module 2: Performance Fundamentals of RAG Pipelines

1. Retrieval pipeline analysis
2. Generation pipeline analysis
3. Latency sources identification
4. Throughput measurement techniques
5. Resource utilization assessment
6. Bottleneck identification methodologies

Module 3: Embeddings Optimization

1. Embedding model selection
2. Embedding dimensionality considerations
3. Embedding generation performance
4. Storage optimization techniques
5. Embedding quality versus speed trade-offs
6. Embedding lifecycle management

Module 4: Vector Database Optimization

1. Vector indexing strategies
2. Approximate nearest neighbor optimization
3. Query performance tuning
4. Index maintenance techniques
5. Storage efficiency improvements
6. Scalability optimization approaches

Module 5: Retrieval Performance Optimization

1. Search latency reduction techniques
2. Retrieval precision improvement
3. Hybrid retrieval optimization
4. Query transformation strategies
5. Metadata filtering optimization
6. Retrieval caching techniques

Module 6: Reranking and Context Optimization

1. Reranking performance considerations
2. Multi-stage retrieval optimization
3. Context window management
4. Context compression techniques
5. Relevance optimization strategies
6. Cost-efficient reranking approaches

Module 7: LLM Inference Optimization

1. Model selection strategies
2. Token utilization optimization
3. Prompt efficiency techniques
4. Inference latency reduction
5. Response generation tuning
6. Cost optimization methodologies

Module 8: Caching and Acceleration Techniques

1. Query caching strategies
2. Response caching mechanisms
3. Embedding caching approaches
4. Distributed cache architectures
5. Cache invalidation techniques
6. Performance acceleration patterns

Module 9: Scalability and Distributed Architectures

1. Horizontal scaling strategies
2. Distributed retrieval systems
3. Load balancing techniques
4. High-availability architectures
5. Capacity planning methodologies
6. Multi-region deployment considerations

Module 10: Observability and Performance Monitoring

1. RAG observability frameworks
2. Performance telemetry collection
3. Monitoring dashboards
4. Alerting strategies
5. Root cause analysis methodologies
6. Continuous optimization processes

Module 11: Cost Optimization and Operational Excellence

1. Infrastructure cost management
2. Token consumption optimization
3. Resource allocation strategies
4. Performance-cost balancing
5. Operational efficiency metrics
6. Enterprise optimization frameworks

Module 12: RAG Performance Optimization Workshop

1. Retrieval tuning exercises
2. Vector database optimization laboratories
3. Reranking performance assessments
4. Scalability implementation projects
5. Monitoring and observability configuration
6. Final enterprise RAG optimization project