uCertify

Prompt Engineering, Transformers & Applied Generative AI

Master Prompt Engineering, Transformers & Applied Generative AI. In an era of AI-driven development, proficiency in Applied AI Solutions is the definitive gateway to leading the next generation of software engineering.

(PROMPTENG-GENAI.AA1) / ISBN : 979-8-90059-022-6
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About This Course

Move beyond superficial AI interactions to master the architecture behind the intelligence. As engineering shifts toward an AI-first paradigm, the ability to strategically integrate Natural Language Processing into production systems is essential. This course bypasses basic prompts, focusing instead on securing and operationalizing Transformer Models across the entire development lifecycle.

Through uCertify’s immersive labs, you will bridge the gap between theory and deployment. Master the nuances of attention mechanisms, optimize Generative AI Techniques, and design robust LLM Application Architecture. Whether architecting agentic workflows or fine-tuning models, you will gain the hands-on expertise to move sophisticated AI solutions from prototype to production.

Skills You’ll Get

  • Architectural Foundations & NLP: Deconstruct the mechanics of Transformer Models, from encoder-decoder bottlenecks to attention layers. You will apply core Natural Language Processing principles to engineer context-aware systems that move beyond simple pattern matching.
  • Prompt Engineering & System Design: Move into LLM Application Architecture, treating prompts as structured code. By utilizing Generative AI Techniques like ReAct and Chain-of-Thought, you will learn to minimize hallucination and maximize logic in production environments.
  • Operationalizing Applied AI: Transition from local scripts to enterprise-scale Applied AI Solutions. Through 11 specialized Virtual Labs, you will configure hyperparameters and build secure deployment pipelines that integrate seamlessly with legacy software.
  • Security, Ethics, & Validation: Harden your AI implementations with rigorous security guardrails. Using interactive insights and 107 focused assessments, you will learn to audit for bias and ensure every deployment is ethically sound and production-ready.
Download Course Outline

1

Foundations of AI, ML, and Generative Systems

  • 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
2

Evolution of Machine Learning to Deep Learning

  • 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 (Type A): Simulating a Tiny Feed-Forward Network
  • Common Misconceptions and Pitfalls
  • Hands-On Lab Exercise
  • Key Takeaways
3

Development of Generative Models

  • 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

Rise of GPT and the Transformer Revolution

  • 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
5

Inside Transformer Architecture & the GPT Family

  • 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
6

The Prompt Ecosystem

  • 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
7

Prompt Types and When to Use Them

  • 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
8

Tokens and Constraints in Prompt Design

  • 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
9

Efficiency, Syntax and Structure in Prompt Engineering

  • 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
10

Techniques and Strategies for Professional Prompt Engineering

  • 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
11

Tools and Platforms for Prompt Engineering

  • 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
12

Applied Prompt Engineering in Real Products

  • 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 (Type C): Build a Simple Real Chatbot Using Prompts
  • Key takeaways
13

Ethics, Bias & Responsible Prompt Practices

  • 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
14

Cost Management & Prompt Economics

  • 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
15

Future Directions in AI, ML and Prompt Engineering

  • 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
16

Legal and Regulatory Framework for AI

  • 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
17

Build an Enterprise Prompt System (Capstone Project)

  • 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

1

The Prompt Ecosystem

  • Building and Refining High-Impact Summarization Prompts
2

Prompt Types and When to Use Them

  • Applying and Comparing Core Prompt Types
3

Tokens and Constraints in Prompt Design

  • Diagnosing a Business Problem and Simulating Solutions
4

Efficiency, Syntax and Structure in Prompt Engineering

  • Optimizing Prompt Syntax for Maximum Efficiency
5

Techniques and Strategies for Professional Prompt Engineering

  • Building a Multi-Step Prompt Workflow
6

Tools and Platforms for Prompt Engineering

  • Building and Testing a Prompt Framework for a Business Function
7

Applied Prompt Engineering in Real Products

  • Building a Simple Real Chatbot Using Prompts
8

Ethics, Bias & Responsible Prompt Practices

  • Designing Ethical Prompts for Customer-Facing AI
9

Future Directions in AI, ML and Prompt Engineering

  • Charting the Evolution of AI and Prompt Engineering
10

Legal and Regulatory Framework for AI

  • Implementing Trustworthy and Compliant AI Practices
11

Build an Enterprise Prompt System (Capstone Project)

  • Building an Enterprise Prompt System

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This program is ideal for software developers, AI researchers, and technical architects who need to master the practical application of Transformer Models and move beyond simple AI interactions into full-scale application development.

The course focuses on the practical application and implementation of Natural Language Processing frameworks. We prioritize understanding the architecture's components—like self-attention—and how to tune them for better performance in Applied AI Solutions.

We dive deep into Generative AI Techniques such as prompt-tuning and few-shot learning. You will use our 11 Virtual Labs to test model outputs against real-world scenarios, ensuring high-quality and reliable production results.

While we use leading models for demonstration, the curriculum is designed to be model-agnostic. You will learn the universal principles of LLM Application Architecture that can be applied to any modern foundation model, including open-source variants.

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