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Generative AI for Software Developers
Transform your coding workflow and career by mastering Generative AI techniques, the definitive skill set for the modern software developer
- Practice in 36 Laboratorios prácticos — nothing to install
- 12 Lecciones interactivas y 95 topics mapped to the official exam objectives
Intermediate A tu propio ritmo · 1 año de acceso
36 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
12Lecciones interactivas
95Topics
36Laboratorio en vivo
125Tarjetas didácticas
125Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
Are you ready to move beyond basic scripts and truly leverage Generative AI to revolutionize your development process? The role of the software developer is fundamentally changing, demanding specialized knowledge in how to strategically integrate AI into production systems. This highly specialized course moves you past simple helper functions and dives deep into architecting, securing, and operationalizing intelligence across the entire SDLC.
You will master the foundational models (LLMs, SLMs, and LMMs), learn professional Prompt Engineering techniques for maximum efficiency, and explore GenAI Ops to deploy robust AI-powered applications. Whether you are aiming for faster Code Generation, designing complex multi-AI Agents, or leading a team in Model Fine-Tuning, this program provides the practical, hands-on knowledge to design and launch advanced Generative AI solutions from prototype to production.
- Foundations & Code Generation: Master the art and science of Generative AI, distinguishing between various foundation models (LLMs), and leveraging core concepts and tools for efficient and accurate Code Generation to accelerate your daily tasks.
- Architecture & Prompt Engineering: Dive into Generative AI application architectures and design, applying advanced Prompt Engineering techniques and prompt management cycles to optimize model performance across the Software Development Life Cycle (SDLC).
- Integration & Production Readiness: Learn to integrate AI seamlessly into the SDLC, mastering GenAI Ops for operationalizing and scaling Generative AI applications, including crucial Model Fine-Tuning strategies for building well-architected systems.
- Agentic AI, Security, & Ethical AI: Explore the future with Reinforcement Learning and the creation of sophisticated multi-AI Agents, while establishing essential security architecture, guardrails, and Ethical AI practices to mitigate bias and privacy concerns.
Course Highlights
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12 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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36 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
12 Lecciones interactivas · 95 topics01 Preface 5 topics +
- Who is this course for
- Why This course Matters Now
- What this course covers
- Accessing Code
- To get the most out of this book
02 The Art and Science of Generative AI 8 topics · 4 Laboratorio en vivo +
- What is Generative AI?
- Generative AI Use Cases
- Generative AI Benefits
- Myths Around Generative AI
- Challenges of Generative AI
- Generative AI for Software Development
- How Developers Should Evolve with Generative AI
- Summary
4 Laboratorio en vivo in this lesson — see the labs panel →
03 Getting Started with Generative AI 7 topics · 1 Laboratorio en vivo +
- Expanding Your Generative AI Knowledge: SLMs, LLMs, and LMMs
- Foundation Models in Generative AI
- How to Start with Generative AI
- Code Generation Using Generative AI
- Agentic AI Workflows
- How Generative AI is Becoming Democratized
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
04 Generative AI Architecture Fundamentals 8 topics · 2 Laboratorio en vivo +
- Understanding Generative AI Models Architecture
- Category of Generative AI Models and Their Architecture
- Approaches of Generative Models
- Hyperparameter Tuning and Regularization
- Model Evaluation Techniques
- Choosing the Right Generative Model for Specific Use Cases
- Best Practices for Model Evaluation
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
05 Generative AI in Software Development 4 topics · 1 Laboratorio en vivo +
- Impact of Generative AI on Software Development
- Essential Tools and Frameworks for Gen AI-Based Software Application Development
- GenAI Ops: Operationalizing Generative AI Applications
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
06 Prompt Engineering For Software Developers 6 topics · 9 Laboratorio en vivo +
- Why Prompt Engineering?
- Prompt Techniques
- Prompt Use Cases for the Software Development Lifecycle (SDLC)
- Prompt Management Cycle and Best Practices
- Prompt Engineering Tools
- Summary
9 Laboratorio en vivo in this lesson — see the labs panel →
07 Integrating Generative AI into the Software Development Cycle 9 topics · 9 Laboratorio en vivo +
- Industry Study on Developer Productivity with Generative AI
- Transforming Software Development with Generative AI in the SDLC
- Generative AI for Specific Programming Tasks
- End-to-End AI Integration in the SDLC
- Challenges and Tradeoffs in AI Integration
- Key Metrics and KPIs for Measuring AI Impact
- Next Steps: Sustaining and Expanding AI Integration
- The Future Outlook
- Summary
9 Laboratorio en vivo in this lesson — see the labs panel →
08 Ethical and Security Best Practices in Generative AI 14 topics · 2 Laboratorio en vivo +
- Why the New Concerns?
- Bias in AI-Generated Code
- Model Architecture and Optimization Bias
- Human Feedback Bias
- Strategies to Mitigate Bias
- Prompt Safety and Security for Responsible AI
- Intellectual Property (IP) Considerations
- Privacy Concerns in Generative AI
- Key AI Laws and Guidelines
- Security Risks in AI Applications
- Security Architecture for Generative AI Apps
- Guardrails for Secure Use of Generative AI Applications
- Observability from an Ethical AI Perspective
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
09 Generative AI Application Architecture and Design 8 topics · 1 Laboratorio en vivo +
- Principles of Generative AI Application Architecture
- Text Generation Architecture
- Text Summarization Architecture
- Q&A (Question and Answer) App architecture
- Chatbot Architecture
- Image and Video Generation App Architecture
- GenAI Architecture for Industry Use Cases
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
10 Reinforcement Learning and AI Agent Architecture Design 9 topics · 1 Laboratorio en vivo +
- What is Reinforcement Learning?
- Reinforcement Learning with Human Feedback
- Automated Reinforcement Learning (AutoRL)
- GenAI Agents
- Agentic AI
- Building an Intelligent Travel Assistant
- GenAI Multi-Agent Systems
- Function Calling with LLMs
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
11 Well-Architecting and Fine-tuning GenAI Application 6 topics · 1 Laboratorio en vivo +
- What is Model Fine-Tuning?
- Model Evaluation
- LLM Benchmarking
- Building Well-Architected Gen AI Applications
- Well-Architected Framework Pillars for GenAI Applications
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
12 Building a GenAI App from Prototype to Production 11 topics · 5 Laboratorio en vivo +
- Building SkillGenie - Problem Statement
- SkillGenie – Features
- SkillGenie User Journey
- System Design for SkillGenie
- API Design
- Prototype Development
- Safe use of AI and content moderation
- Enhancing SkillGenie outputs using Agentic AI
- Production Launch
- Post-Production Monitoring
- Summary
5 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
36 Laboratorio en vivos- Mastering Generative AI
- Building a Simple Generative App Using LangChain
- Building a Real-Time Customer Service Chatbot
- Building a Content Generation Platform with LangChain
- Getting Started with Generative AI
- Applying Advanced Techniques for Controlling ChatGPT
- Understanding Generative AI Architecture Fundamentals
- Understanding Generative AI in Software Development
- Exploring Different Prompt Styles
- Exploring Advanced Prompting Techniques
- Applying Basic AI Prompting to SDLC Activities
- Calling an LLM API Using Python
- Building a Conversational App Using Python
- Building a Simple Q&A Application
- Creating a Custom Example Selector from Scratch
- Crafting a Few-Shot Prompt Template for Question Answering
- Creating a Chat Prompt Template
- Exploring AI-Prompt Use Cases Across the SDLC
- Integrating Generative AI into the SDLC
- Building a Sentiment Analysis App Using Conditional Chains
- Creating a Recommendation Engine Using LLM Agents
- Using LangChain for a Retrieval Task
- Building Structured Movie Data Using PydanticOutputParser
- Building and Executing LCEL Chains
- Handling Large Datasets with Chains
- Building Data Ingestion Pipeline for RAG
- Shaping the Future of Prompt Engineering
- Managing Agent Inputs and Outputs in LangChain
- Designing and Architecting Generative AI Applications
- Generating and Learning with Agentic AI
- Tuning and Benchmarking GenAI
- Developing SkillGenie
- Building an End-to-End Agent Application
- Generating Content Using AI Agents
- Implementing Tool-Calling Agents in LangChain
- Creating a Custom Agent in LangChain
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
Who should take the Generative AI for Software Developers course?+
This program is ideal for software developers, application architects, MLOps engineers, and anyone responsible for integrating or deploying Generative AI features into commercial software applications.
How deep does the course go into prompt optimization?+
It covers the full Prompt Engineering spectrum, from basic prompting techniques to advanced concepts like ReAct, Self-Consistency, and RAG, ensuring you can optimize LLMs for high-quality, reliable outputs in the SDLC.
Does the training cover emerging architectures like AI Agents?+
Yes, the course includes dedicated content on Reinforcement Learning (RL) and AI Agents, focusing on how to design and implement sophisticated multi-agent systems and leveraging function calling with large language models.
Is the focus on theory or practical deployment?+
The course is heavily focused on practice, covering application architecture, Model Fine-Tuning, and providing hands-on labs that guide you through building a production-ready Generative AI application from prototype to post-production monitoring.
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CTA—Ready to Build Certified Generative AI Applications?
The future of software is augmented. Start your journey to becoming a lead Generative AI developer and transform your team’s capabilities with this essential program.
- 1 año de acceso completo
- 36 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito