Generative AI Training Course

Lesson 1 • Major Model Families Overview

Surveys GANs, VAEs, diffusion models, and large language models at a conceptual level. Prepares learners to study each family in depth in later chapters.

Lesson 2 • Brief History of Generative Models

Traces generative AI from early statistical models to modern neural architectures. Provides historical context that explains why current tools are designed as they are.

Lesson 3 • Responsible AI Principles from the Start

Establishes ethical responsibilities, bias risks, and transparency expectations before any hands-on work begins. Frames responsible practice as integral, not optional.

Lesson 4 • What Generative AI Actually Is

Defines generative AI by contrasting it with discriminative and rule-based systems. Establishes the conceptual baseline needed for every subsequent chapter.

Lesson 1 • Evaluating Language Model Performance

Introduces benchmarks, perplexity, and human evaluation methods for assessing LLM quality. Enables learners to critically interpret model comparison claims.

Lesson 2 • Reinforcement Learning from Human Feedback

Details how human preference data shapes model outputs through reward modeling and policy optimization. Explains why aligned models behave differently from base models.

Lesson 3 • Transformer Architecture Explained

Breaks down attention mechanisms, encoder-decoder structures, and positional encoding. Grounds learners in the architecture that powers most modern language models.

Lesson 4 • Tokenization and Embeddings

Explains how text is converted into tokens and numerical vectors before model processing. Directly impacts understanding of context windows and model limitations.

Lesson 1 • Zero-Shot and Few-Shot Prompting

Contrasts prompting with no examples versus providing curated demonstrations. Teaches when each approach maximizes output quality and consistency.

Lesson 2 • Prompt Iteration and Testing Workflows

Introduces systematic prompt testing, version control, and regression evaluation. Transforms ad hoc prompting into a disciplined engineering practice.

Lesson 3 • Chain-of-Thought and Reasoning Prompts

Applies step-by-step reasoning instructions to improve model accuracy on complex tasks. Directly extends few-shot techniques with structured reasoning scaffolds.

Lesson 4 • Advanced Prompt Patterns and Anti-Patterns

Catalogs reusable prompt design patterns and common failure modes to avoid. Equips learners to handle edge cases and adversarial inputs confidently.

Lesson 1 • Summarization and Information Extraction

Teaches extractive and abstractive summarization techniques for long documents and data-rich sources. Enables rapid knowledge synthesis from large information volumes.

Lesson 2 • Structured Content and Data-to-Text

Generates structured outputs such as reports, tables, and narratives from raw data inputs. Connects generative AI to business intelligence and reporting workflows.

Lesson 3 • Translation and Multilingual Content

Explores AI-powered translation, localization, and cross-lingual content adaptation. Addresses accuracy, cultural nuance, and post-editing workflows.

Lesson 4 • Content Quality and Hallucination Control

Identifies hallucination patterns and applies mitigation strategies including grounding and retrieval. Ensures outputs meet professional accuracy and reliability standards.

Lesson 1 • Text-to-Image Prompting Techniques

Applies prompt engineering principles specifically to image generation, covering style, composition, and quality modifiers. Extends text prompting skills into the visual domain.

Lesson 2 • How Image Generation Models Work

Explains diffusion models, GANs, and autoregressive image models at a mechanistic level. Provides the conceptual grounding needed to use and troubleshoot image tools effectively.

Lesson 3 • Ethics and Rights in Visual AI

Addresses deepfake risks, consent, copyright, and representation bias in AI-generated imagery. Ensures learners apply visual AI tools within ethical and legal boundaries.

Lesson 4 • Multimodal Models and Vision-Language Tasks

Introduces models that process both text and images for tasks like visual question answering and captioning. Expands learner capability beyond single-modality workflows.

Lesson 1 • Retrieval-Augmented Generation Systems

Builds RAG pipelines that ground model outputs in external knowledge bases. Directly addresses hallucination and knowledge-cutoff limitations introduced in earlier chapters.

Lesson 2 • Deploying and Monitoring AI Applications

Covers containerization, API serving, latency optimization, and production monitoring for AI apps. Prepares learners to take prototypes into reliable production environments.

Lesson 3 • AI Agents and Tool Use

Designs autonomous agents that plan, use tools, and execute multi-step tasks. Extends single-turn prompting into dynamic, goal-directed AI workflows.

Lesson 4 • Orchestration Frameworks and Pipelines

Introduces frameworks for chaining prompts, managing state, and orchestrating complex AI workflows. Enables learners to build maintainable, production-grade AI pipelines.

Lesson 1 • Dataset Curation and Preparation

Covers data collection, cleaning, formatting, and quality filtering for fine-tuning datasets. High-quality data is the single largest determinant of fine-tuning success.

Lesson 2 • Evaluating and Iterating Fine-Tuned Models

Applies task-specific evaluation metrics and human review to assess fine-tuned model quality. Closes the loop between training and deployment with structured iteration.

Lesson 3 • Full Fine-Tuning Workflows

Walks through end-to-end supervised fine-tuning including hyperparameter selection and training loops. Builds practical execution skills for standard fine-tuning scenarios.

Lesson 4 • When and Why to Fine-Tune

Establishes decision criteria for choosing fine-tuning over prompting or RAG. Prevents costly misapplication of fine-tuning where simpler approaches suffice.

Lesson 1 • Data Privacy and Security in AI Systems

Covers data minimization, anonymization, access controls, and prompt injection defenses for AI systems. Ensures learners protect sensitive information throughout the AI lifecycle.

Lesson 2 • Measuring AI ROI and Continuous Improvement

Establishes KPIs, value measurement frameworks, and feedback loops for ongoing AI program improvement. Enables learners to demonstrate and sustain business value from AI investments.

Lesson 3 • Bias Auditing and Fairness Testing

Applies systematic auditing methods to detect and reduce bias in generative AI outputs. Extends foundational ethics concepts into rigorous, repeatable testing practice.

Lesson 4 • AI Risk Management and Compliance

Identifies technical, reputational, and regulatory risks of generative AI and maps mitigation strategies. Prepares learners to satisfy compliance requirements across industries.