PROMPTENG-GENAI.AA1
Prompt Engineering, Transformers & Applied Generative AI
Master Prompt Engineering, Transformers, and Applied Generative AI to build robust, cost-effective LLM applications.
- Practice in 54 Laboratorios prácticos — nothing to install
- 17 Lecciones interactivas y 140 topics mapped to the official exam objectives
Beginner A tu propio ritmo · 1 año de acceso
54 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
17Lecciones interactivas
140Topics
54Laboratorio en vivo
16Vídeos
75Tarjetas didácticas
75Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
This course offers a rigorous, technical deep dive into prompt engineering, transformers, and the application of generative AI. We dissect the evolution from foundational AI and machine learning to deep learning, culminating in modern generative models and the Transformer architecture that powers GPT.
You'll master prompt design and understand token economics, constraints, and advanced strategies, such as multi-agent orchestration. Learn to build robust LLM application architectures, integrating tools like OpenAI and LangChain.
We tackle real-world challenges: managing costs, mitigating prompt-induced bias, and navigating legal frameworks. This isn't about theoretical perfection; it's about building effective, responsible AI systems, acknowledging their limitations and trade-offs.
- Design and optimize prompts for Large Language Models (LLMs): Master the anatomy of prompts, various prompt types (e.g., zero-shot, few-shot, chain-of-thought), and iterative refinement techniques to elicit precise, desired outputs from generative AI models, understanding token limits and cost implications.
- Implement and manage Transformer-based Generative AI architectures: Gain a deep understanding of Transformer mechanics, including self-attention, tokenization, and embeddings, to effectively integrate and fine-tune models like GPT within complex LLM application architectures, recognizing scaling law impacts.
- Develop and deploy real-world Generative AI applications: Apply prompt engineering principles to build practical solutions for content generation, chatbots, customer support, and Retrieval-Augmented Generation (RAG) systems while navigating platform-specific tools and integration challenges.
- Evaluate and mitigate ethical, biased, and cost considerations in AI systems: Critically assess prompt-induced bias, data privacy, and fairness in AI outputs. Learn strategies for cost management through efficient prompt design and model selection, ensuring responsible and economically viable LLM deployments.
Course Highlights
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17 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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54 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
17 Lecciones interactivas · 140 topics01 Foundations of AI, ML, and Generative Systems 9 topics · 4 Laboratorio en vivo +
- Why Foundations Matter?
- A Short History of Artificial Intelligence
- Understanding Machine Learning: From Instructions to Experience
- Deep Learning: How Neural Networks See Patterns
- The Emergence of Generative AI
- A Unified View: AI, ML, DL and Generative AI
- Troubleshooting Misconceptions
- Hands-On Lab Exercise
- Key takeaways
4 Laboratorio en vivo in this lesson — see the labs panel →
02 Evolution of Machine Learning to Deep Learning 9 topics · 3 Laboratorio en vivo +
- From Rule-Based AI to Statistical Learning
- The Shift to Machine Learning (The Statistical Era)
- Neural Networks and Backpropagation: The First Major Breakthrough
- Big Data and GPU/TPU Acceleration: The Deep Learning Revolution
- Scaling Laws and the Emergence of Modern AI
- Hands-On Lab Exercise: Simulating a Tiny Feed-Forward Network
- Common Misconceptions and Pitfalls
- Hands-On Lab Exercise
- Key Takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
03 Development of Generative Models 7 topics · 4 Laboratorio en vivo +
- Why Generative Models Were Developed
- Generative vs. Discriminative Models
- Classical Generative Models
- Autoregressive LLMs
- Summary Diagram: Generative Model Family Tree
- Hands-On Lab Exercise
- Key takeaways
4 Laboratorio en vivo in this lesson — see the labs panel →
04 Rise of GPT and the Transformer Revolution 8 topics · 4 Laboratorio en vivo +
- Why Transformers Solved Long-Range Dependencies
- Self-Attention, Multi-Head Attention and Positional Encoding
- Evolution of GPT
- Breakthrough Models
- Impact of scaling laws
- Simplified Transformer Block Diagram
- Hands-On Lab Exercise
- Key takeaways
4 Laboratorio en vivo in this lesson — see the labs panel →
05 Inside Transformer Architecture & the GPT Family 11 topics · 4 Laboratorio en vivo +
- Tokenization: Breaking Language Into Pieces
- Embeddings: Turning Tokens Into Meaning
- Attention: Where the Model Looks to Understand Context
- Logits: How the Model Predicts the Next Token
- How GPT Is Trained: Data, Compute, and Loss
- Transfer Learning and Fine-Tuning
- Fine-Tuning LLMs in the Enterprise
- Comparing GPT With Earlier AI Models
- Real-World Applications of GPT
- Hands-On Lab (Type A): Visualizing Tokens & Attention
- Key takeaways
4 Laboratorio en vivo in this lesson — see the labs panel →
06 The Prompt Ecosystem 9 topics · 4 Laboratorio en vivo +
- What Is a Prompt Ecosystem?
- How Prompts Influence AI Outcomes?
- Anatomy of a Prompt
- Types of Prompt Structures
- Iteration, Refinement, and Constraints
- Hands-On Lab (Type A): Build & Refine a High-Impact Prompt
- Troubleshooting Prompt Issues
- Hands-On Lab Exercise
- Key takeaways
4 Laboratorio en vivo in this lesson — see the labs panel →
07 Prompt Types and When to Use Them 9 topics · 3 Laboratorio en vivo +
- Open-Ended vs. Closed-Ended Prompts
- Exploratory Prompts
- Multi-Modal Prompts
- Contextual Prompts
- Procedural and Chain Prompts
- Adaptive Prompts (Dynamic State Prompts)
- Hands-On Lab (Type B): Classify Prompt Types from Real Examples
- Hands-On Lab Exercise
- Key takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
08 Tokens and Constraints in Prompt Design 7 topics · 3 Laboratorio en vivo +
- What Is a Token and Why Does it Matter?
- Tokenization in the Real World
- Token Limits, Cost, and Memory
- Designing Effective Prompts Under Constraints
- Case Study: GPT-4 Token Optimization
- Hands-On Lab (Type B): Rewrite Long Prompts into Optimized Prompts
- Key takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
09 Efficiency, Syntax and Structure in Prompt Engineering 8 topics · 3 Laboratorio en vivo +
- Why Syntax Changes Outputs?
- The Role of Punctuation, Lists, and Sequencing
- Meta-Prompting: Prompts About Prompts
- Balancing Simplicity and Complexity
- Efficient Prompts for Performance and Cost
- Hands-On Lab (Type B): Syntax Optimization & Efficiency
- Checklist: Syntax Best Practices
- Key takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
10 Techniques and Strategies for Professional Prompt Engineering 8 topics · 3 Laboratorio en vivo +
- Iterative Refinement
- Prompt Chaining and Multi-Step Reasoning
- Multi-Agent Orchestration with Prompts
- Multi-Turn Conversation Strategies
- Zero-Shot and Few-Shot Prompting
- Prompt Tuning and Embeddings
- Hands-On Lab (Type C): Build a Mini Multi-Step Prompt Workflow
- Key takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
11 Tools and Platforms for Prompt Engineering 7 topics · 3 Laboratorio en vivo +
- OpenAI: ChatGPT, Playground, and API
- Google Gemini, Microsoft Copilot, Anthropic Claude, and Meta LLaMA
- HuggingFace and LangChain
- Writing, Testing, and Debugging Prompts
- Integration of Prompts Into Workflows and Automation
- Hands-On Lab (Type B): Build a Simple Assistant in Playground
- Key takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
12 Applied Prompt Engineering in Real Products 10 topics · 3 Laboratorio en vivo +
- Content Generation Systems
- Chatbots: The Most Common Applied Use Case
- Customer Support Flows
- Documentation Automation
- Retrieval-Augmented Generation (RAG) Fundamentals
- Interactive Querying Systems
- Advanced Embeddings and Document Chunking
- Multi-Modal Use Cases
- Project: Build a Simple Real Chatbot Using Prompts
- Key takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
13 Ethics, Bias & Responsible Prompt Practices 7 topics · 3 Laboratorio en vivo +
- Fairness, Transparency, and Accountability in Prompting
- Prompt-Induced Bias
- Data Privacy Issues in Prompt Engineering
- Avoiding Harmful Instructions
- Case Studies
- Ethical Prompting Checklist
- Key Takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
14 Cost Management & Prompt Economics 8 topics · 2 Laboratorio en vivo +
- API Pricing and Token Economics
- Reducing Cost via Better Prompt Design
- Batch Prompting and Caching
- Model Selection as a Cost Strategy
- Cloud, Multi-Cloud, and On-Prem Considerations
- LLMOps and Enterprise Deployment
- Cost-Optimized Prompting Framework
- Key takeaways
2 Laboratorio en vivo in this lesson — see the labs panel →
15 Future Directions in AI, ML and Prompt Engineering 7 topics · 2 Laboratorio en vivo +
- Next-Generation Model Architectures (Beyond Transformers)
- Multi-Agent Systems (Teams of AIs Working Together)
- Personalized AI and Continuous Context Memory
- AR/VR and Evolution of Prompt-based Interaction
- AI for Social Good
- Infographic: "What’s Coming After GPT-5?"
- Key takeaways
2 Laboratorio en vivo in this lesson — see the labs panel →
16 Legal and Regulatory Framework for AI 9 topics · 3 Laboratorio en vivo +
- National and International AI Laws
- Intellectual Property (IP) in AI-Generated Content
- Data Privacy and Security Requirements
- Liability in AI Outputs
- Governance of Prompt-Driven Systems
- Testing, Monitoring, and Evaluation for LLM Systems
- Risk Management and Compliance
- Global AI Safety and Accountability Movement
- Key takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
17 Build an Enterprise Prompt System (Capstone Project) 7 topics · 3 Laboratorio en vivo +
- Define a Real Business Problem
- Build a Prompt Framework
- Implement Workflow and Iterations
- Test Cross-Platform
- Evaluate Ethics, Cost and Performance
- Present the Solution
- Key takeaways
3 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
54 Laboratorio en vivos- Defending the Future of Intelligence
- Understanding AI Systems for Better Decision-Making
- Creating a Machine Learning Classification Pipeline
- Building Machine Learning Classification Workflows
- Architecting the Adaptive Defense
- Evolving Fraud Detection from Rules to Learning Systems
- Creating a Convolutional Neural Network for Digit Classification
- Optimizing Enterprise AI Architectures
- Transitioning from Predictive Models to Generative Intelligence
- Creating a Transformer-Based NLP Pipeline
- Fine-Tuning Sentiment Models
- Architectural Defense: Multi-Head Attention
- Upgrading NLP Systems at LexiCorp
- Creating a Prompt Engineering Workflow Using LLMs
- Optimizing Prompts for Business AI Tasks
- Understanding Tokenization in LLM
- Architecting for Regulatory Compliance
- Visualizing Tokenization, Embeddings, and Attention in Transformer Models
- Exploring Tokenization and Attention
- Architecting Secure Financial AI
- Building and Refining High-Impact Summarization Prompts
- Creating Context-Aware Assistants
- Designing Multi-Turn Conversations and Chaining Prompts with LLMs
- Deconstructing the Productivity Paradox
- Applying and Comparing Core Prompt Types
- Building an Enterprise Multi-Modal AI Assistant
- Defending the Token Budget
- Managing Token Efficiency at InsightAI
- Diagnosing a Business Problem and Simulating Solutions
- Engineering Reliable SOP Automation
- Understanding Prompt Failure at InsightCorp
- Optimizing Prompt Syntax for Maximum Efficiency
- Architecting the Intelligent Feedback Pipeline
- Optimizing Enterprise AI Workflows with Structured Prompting
- Building a Multi-Step Prompt Workflow
- Architecting Enterprise AI Strategy
- Designing Enterprise AI Workflows with Generative AI Platforms
- Building and Testing a Prompt Framework for a Business Function
- Architecting the Onboarding Assistant
- Building Reliable Enterprise Knowledge Systems with RAG
- Building a Simple Real Chatbot Using Prompts
- Architecting Ethical AI Personas
- Designing Ethical Prompts for Customer-Facing AI
- Strengthening Ethical Prompting at SecureMind AI
- Architecting Value in AI Operations
- Optimizing AI Costs at IntelliServe
- Architecting Resilience Beyond Monoliths
- Charting the Evolution of AI and Prompt Engineering
- Securing Rights for AI-Assisted Art
- Implementing Trustworthy and Compliant AI Practices
- Governing AI Systems for Responsible Deployment
- Architecting Resilience Under Pressure
- Designing AI Workflows for Better Automation
- Building an Enterprise Prompt System
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What is Prompt Engineering and why is it critical for modern AI applications? +
Prompt Engineering is the art and science of crafting effective inputs (prompts) to guide Generative AI models, like LLMs, to produce desired outputs. It's critical because poorly designed prompts lead to irrelevant, biased, or costly results, directly impacting the performance and utility of any LLM application.
How does this course address the technical aspects of Transformers and GPT?+
We dive deep into the Transformer architecture, explaining self-attention, multi-head attention, positional encoding, tokenization, and embeddings. You'll understand how GPT models are trained and how these foundational concepts directly influence prompt design and LLM behavior, moving beyond surface-level interaction.
Will I learn to build actual Generative AI applications? +
Absolutely. The course culminates in a capstone project where you define a business problem and build an enterprise prompt system. We cover applied prompt engineering in real products like chatbots, content generation, and RAG systems, focusing on practical implementation and workflow integration.
What are the key limitations or challenges covered in prompt engineering? +
We're brutally honest about limitations. You'll learn about token limits, cost implications (API pricing, token economics), prompt-induced bias, data privacy concerns, and the trade-offs between prompt complexity and model performance. The goal is to build resilient systems, not perfect ones.
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- 1 año de acceso completo
- 54 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito