What you'll learn
- Generative AI landscape
- Large Language Models
- Prompt Engineering
- Multimodal AI
- Retrieval-Augmented Generation (RAG)
- API-based automation & agents
- AI risk, ethics & governance
Curriculum
M1 | The generative AI landscape5 topics
- What "generative" means; discriminative vs generative AI
- Model families: text (LLMs), image, audio/voice, video, multimodal
- Capability and limitation map
- Choosing the right tool for a task
- Quiz: M1 | The generative AI landscape
M2 | How LLMs actually work6 topics
- Tokens, parameters and training vs inference
- Context windows and memory limits
- Temperature, randomness and determinism
- Hallucination and knowledge cut-offs
- End-to-end prompt-to-token walkthrough
- Quiz: M2 | How LLMs actually work
M3 | Prompt engineering fundamentals5 topics
- Anatomy of a good prompt: role, task, context, format, constraints
- Zero-shot vs few-shot prompting
- System prompts, personas and rules
- Iterating and debugging prompts
- Quiz: M3 | Prompt engineering fundamentals
M4 | Advanced prompting5 topics
- Chain-of-thought and step-by-step reasoning
- Task decomposition and modular prompting
- Structured output: JSON, tables and schemas
- Self-critique and prompt libraries/templates
- Quiz: M4 | Advanced prompting
M5 | Multimodal AI in practice5 topics
- Image generation and prompt structure
- Document and PDF understanding
- Voice, transcription and audio models
- Cross-format content pipelines
- Quiz: M5 | Multimodal AI in practice
M6 | Grounding AI in your data (RAG)5 topics
- Why models hallucinate on private or recent data
- RAG workflow: retrieve → augment → answer → cite
- Embeddings, vector search and chunking
- Designing grounded assistants with guardrails
- Quiz: M6 | Grounding AI in your data (RAG)
M7 | Building with the API & automation5 topics
- Understanding API calls, keys and endpoints
- No-code AI automation workflows
- Light coding example for custom integration
- Cost, rate limits and operational considerations
- Quiz: M7 | Building with the API & automation
M8 | AI agents & workflows5 topics
- What an agent is and when to use one
- Tool-use orchestration and planning loops
- Human-in-the-loop checkpoints
- Designing safe, task-specific agents
- Quiz: M8 | AI agents & workflows
About this course
A 35-hour flagship course that takes learners from basic awareness of ChatGPT to confidently designing, prompting and deploying generative-AI solutions—culminating in a portfolio-grade capstone build.