Visão Geral

Este curso aborda o planejamento, desenvolvimento, implantação e operação de soluções corporativas baseadas em Retrieval-Augmented Generation (RAG). O participante aprenderá a construir plataformas empresariais capazes de integrar Large Language Models (LLMs) com bases de conhecimento corporativas, documentos, sistemas internos e fontes externas de informação. O curso explora arquiteturas escaláveis, governança de conhecimento, segurança, observabilidade e melhores práticas para implementação de soluções RAG em larga escala, alinhadas às necessidades de negócios e conformidade corporativa.

Objetivo

Após realizar este curso, você será capaz de:

• Projetar soluções RAG para ambientes corporativos
• Integrar modelos de linguagem a fontes de conhecimento empresariais
• Implementar pipelines de ingestão, recuperação e geração em escala
• Aplicar controles de segurança, governança e conformidade
• Monitorar e otimizar desempenho, custo e qualidade das soluções
• Operar plataformas RAG alinhadas aos requisitos estratégicos da organização

Publico Alvo

• Arquitetos de Soluções
• Engenheiros de IA Generativa
• Desenvolvedores de Software
• Engenheiros de Machine Learning
• Profissionais de LLMOps e Platform Engineering
• Líderes técnicos envolvidos em projetos corporativos de IA

Pre-Requisitos

• Conhecimentos equivalentes ao curso RAG Fundamentals
• Familiaridade com Large Language Models e IA Generativa
• Conhecimentos básicos de arquitetura de software
• Experiência com APIs e integração de sistemas é recomendada

Conteúdo Programatico

Module 1: Introduction to Enterprise RAG Solutions

1. Enterprise knowledge management challenges
2. Evolution of RAG architectures
3. Business value of enterprise RAG
4. Corporate use cases and adoption patterns
5. Enterprise AI strategy alignment
6. RAG solution lifecycle overview

Module 2: Enterprise Knowledge Architecture

1. Corporate knowledge ecosystems
2. Structured and unstructured data sources
3. Enterprise content management
4. Knowledge governance fundamentals
5. Information lifecycle management
6. Data quality and knowledge accuracy

Module 3: Document Processing and Ingestion Pipelines

1. Enterprise document ingestion architectures
2. Data extraction techniques
3. Document enrichment strategies
4. Chunking and segmentation approaches
5. Metadata management
6. Automated ingestion workflows

Module 4: Enterprise Search and Retrieval

1. Retrieval architecture design
2. Semantic search implementation
3. Hybrid search strategies
4. Context selection optimization
5. Relevance ranking techniques
6. Enterprise retrieval governance

Module 5: Vector Databases and Knowledge Storage

1. Enterprise vector database architectures
2. Index management strategies
3. Scalability and performance considerations
4. Multi-tenant knowledge repositories
5. Data synchronization techniques
6. Storage optimization practices

Module 6: LLM Integration and Response Generation

1. Enterprise LLM integration patterns
2. Context augmentation techniques
3. Prompt orchestration strategies
4. Response generation optimization
5. Hallucination reduction approaches
6. Source attribution mechanisms

Module 7: Security and Access Control

1. Enterprise security requirements
2. Identity and access management integration
3. Role-based knowledge access
4. Data privacy protection
5. Secure retrieval architectures
6. Compliance and audit controls

Module 8: Governance and Responsible AI

1. AI governance frameworks
2. Knowledge governance models
3. Responsible AI principles
4. Regulatory compliance requirements
5. Risk management strategies
6. Auditability and accountability

Module 9: Observability and Quality Management

1. RAG observability fundamentals
2. Retrieval performance monitoring
3. Response quality evaluation
4. Groundedness assessment
5. Operational dashboards
6. Continuous improvement practices

Module 10: Scalability and Enterprise Operations

1. High-availability architectures
2. Distributed RAG systems
3. Capacity planning methodologies
4. Performance optimization techniques
5. Cost management strategies
6. Enterprise operational models

Module 11: Industry Use Cases and Solution Patterns

1. Customer support knowledge assistants
2. Legal and compliance solutions
3. Enterprise search platforms
4. Healthcare knowledge systems
5. Financial services applications
6. Internal productivity assistants

Module 12: Enterprise RAG Solutions Workshop

1. Enterprise knowledge architecture design
2. Document ingestion implementation
3. Secure retrieval configuration
4. Quality and performance optimization
5. Governance and observability validation
6. Final enterprise RAG solution project