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Prompt Engineering, Transformers & Applied Generative AI

Master Prompt Engineering, Transformers, and Applied Generative AI to build robust, cost-effective LLM applications. 

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

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.

Skills You’ll Get

  • 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.
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

Success Stories in Action

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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.

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.

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.

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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