Visão Geral

Este curso aprofunda os conceitos e técnicas avançadas de Engenharia de Prompts para Large Language Models (LLMs) e sistemas de Inteligência Artificial Generativa. O participante aprenderá a projetar prompts sofisticados, construir fluxos complexos de interação, desenvolver estratégias de raciocínio estruturado, criar sistemas de prompts reutilizáveis e otimizar resultados para aplicações corporativas e técnicas. O curso também aborda governança, segurança, avaliação de desempenho e integração de prompts em arquiteturas modernas de IA.

Objetivo

Após realizar este curso, você será capaz de:

• Desenvolver prompts avançados para cenários complexos de negócio e tecnologia
• Aplicar técnicas sofisticadas de raciocínio, decomposição e resolução de problemas com IA
• Construir fluxos de prompts reutilizáveis e escaláveis
• Implementar estratégias de validação, avaliação e otimização de respostas geradas por IA
• Projetar interações robustas para aplicações corporativas baseadas em LLMs
• Aplicar práticas de segurança, governança e mitigação de riscos relacionados à Engenharia de Prompts

Publico Alvo

• Desenvolvedores de software
• Engenheiros de IA e Machine Learning
• Arquitetos de soluções
• Analistas de dados e negócios avançados
• Profissionais de automação e inovação digital
• Usuários avançados de Inteligência Artificial Generativa

Pre-Requisitos

• Conhecimento dos fundamentos de IA Generativa
• Experiência prévia com ferramentas de LLMs e chatbots de IA
• Conhecimentos equivalentes ao curso Prompt Engineering Fundamentals
• Familiaridade com processos de negócios ou desenvolvimento de software

Conteúdo Programatico

Module 1: Advanced Foundations of Prompt Engineering

1. Evolution of prompt engineering practices
2. Advanced LLM behavior concepts
3. Understanding model reasoning capabilities
4. Prompt architecture principles
5. Enterprise prompting strategies
6. Designing for consistency and reliability

Module 2: Prompt Design Patterns

1. Reusable prompt patterns
2. Modular prompt construction
3. Dynamic prompt generation
4. Context layering techniques
5. Prompt abstraction methods
6. Pattern libraries and frameworks

Module 3: Advanced Reasoning Techniques

1. Chain-of-thought methodologies
2. Tree-of-thought concepts
3. Structured reasoning workflows
4. Multi-step problem decomposition
5. Analytical prompting techniques
6. Complex decision-support prompting

Module 4: Role-Based and Multi-Persona Prompting

1. Persona engineering concepts
2. Expert simulation techniques
3. Multi-role collaboration prompts
4. Stakeholder-oriented prompting
5. Perspective analysis methods
6. Scenario-based reasoning

Module 5: Prompt Chaining and Workflow Orchestration

1. Sequential prompt workflows
2. Multi-stage task execution
3. Context propagation techniques
4. Prompt pipelines
5. Workflow automation integration
6. Enterprise orchestration patterns

Module 6: Structured Output and Data Extraction

1. Advanced structured output generation
2. JSON and schema-based outputs
3. Information extraction workflows
4. Classification and categorization prompts
5. Validation strategies
6. Data transformation techniques

Module 7: Prompt Engineering for Enterprise Applications

1. Knowledge management assistants
2. Business analysis workflows
3. Customer support automation
4. Executive reporting assistance
5. Content generation systems
6. Process optimization use cases

Module 8: Prompt Engineering for Developers

1. Code generation optimization
2. Software architecture assistance
3. Documentation automation
4. Test generation workflows
5. API interaction strategies
6. Development productivity enhancements

Module 9: Prompt Engineering for RAG and AI Agents

1. Prompting in RAG architectures
2. Context retrieval optimization
3. Agent instruction design
4. Tool-use prompting strategies
5. Autonomous workflow prompting
6. Multi-agent communication patterns

Module 10: Prompt Evaluation and Optimization

1. Prompt testing methodologies
2. Output quality assessment
3. Evaluation metrics and scoring
4. Prompt benchmarking techniques
5. A/B testing approaches
6. Continuous improvement processes

Module 11: Security, Governance and Risk Management

1. Prompt injection threats
2. Jailbreak attack awareness
3. Sensitive data protection
4. Governance frameworks for prompting
5. Compliance and audit considerations
6. Responsible AI practices

Module 12: Enterprise Prompt Engineering Capstone

1. Enterprise prompt architecture workshop
2. Advanced reasoning exercises
3. AI agent instruction design project
4. Prompt optimization laboratory
5. Governance and security assessment
6. Final advanced prompt engineering project