Artificial Intelligence: Intermediate Concepts
Go beyond basic prompting: tokens, transformers, RAG, AI agents, multimodal AI, limitations, and the 2026 AI landscape — for learners ready to go deeper.
This course includes
About this course
This intermediate course goes beyond the basics of using AI chatbots into how modern AI systems actually work: tokens and transformers, the training pipeline, prompt engineering techniques, embeddings and RAG, AI agents and tool use, multimodal models, and the honest limitations and ethics of current systems.
Grounded in the current 2026 AI landscape — agentic AI adoption, new protocols like MCP, and emerging governance — this course is built for learners who already understand AI basics and want a deeper, more technical and practical understanding.
What you'll learn
Explain how tokens, transformers, and self-attention allow LLMs to process language
Describe the pretraining, fine-tuning, and RLHF stages of building a model
Apply zero-shot, few-shot, chain-of-thought, and system prompt techniques deliberately
Explain embeddings, vector databases, and how RAG grounds AI answers in external knowledge
Understand how AI agents use function calling, tool use, and protocols like MCP to take action
Recognize multimodal AI capabilities and the honest limitations of current models
Apply ethical and responsible-use practices, informed by the 2026 AI landscape
Course Content
01 Module 1: How Large Language Models Actually Work 3 lessons
Tokens, transformers, and the training pipeline behind modern AI
- 1.1 From Text to Tokens: How Models Read
- 1.2 The Training Pipeline: Pretraining, Fine-Tuning, RLHF
- Module 1 Check
02 Module 2: Prompt Engineering for Intermediate Users 3 lessons
Structuring prompts for reliable, high-quality output
- 2.1 Zero-Shot, Few-Shot, and Chain-of-Thought
- 2.2 System Prompts, Role Framing, and Iteration
- Module 2 Check
03 Module 3: Embeddings, RAG, and Vector Search 3 lessons
How AI systems retrieve and reason over external knowledge
- 3.1 What Are Embeddings?
- 3.2 RAG: Retrieval-Augmented Generation
- Module 3 Check
04 Module 4: AI Agents and Tool Use 3 lessons
How models act in the world through function calling and agentic loops
- 4.1 From Chatbot to Agent: Tool Use and Function Calling
- 4.2 MCP, Multi-Agent Systems, and Autonomy Levels
- Module 4 Check
05 Module 5: Multimodal AI, Limitations, and Bias 3 lessons
Vision, audio, image generation, and the honest limits of current AI
- 5.1 Beyond Text: Multimodal Models
- 5.2 Honest Limitations: Hallucination, Bias, and Context Limits
- Module 5 Check
06 Module 6: The 2026 AI Landscape, Ethics, and Practical Skills 3 lessons
Where AI stands today, governance, and how to build your own AI literacy
- 6.1 The State of AI in 2026
- 6.2 Ethics, Responsible Use, and Building Your AI Literacy
- Final Assessment: Intermediate AI