AI & RAG Fundamentals: From Theory to Responsible Implementation
Master AI concepts and RAG architecture in 8 focused modules
Prerequisites
- No prior AI or machine learning knowledge required. Basic understanding of software development concepts is helpful but not mandatory. Curiosity and willingness to engage with technical concepts are all you need.
Artificial Intelligence is reshaping how we build software, but understanding how it actually works remains a mystery to many. This fast-track course demystifies AI and Retrieval-Augmented Generation (RAG) through clear explanations and practical insights. You'll journey from AI's seven-decade history to cutting-edge RAG implementations, learning not just what these technologies do, but how and why they work.
In eight carefully structured modules, you'll explore the breakthrough ideas that made modern AI possible, understand how machines actually learn, and discover the economic realities behind every AI call. You'll learn what tokens are and how to control AI behavior, explore models that know their limitations, and understand where AI fails—and why that leads us to RAG. The course culminates with RAG architecture and governance frameworks for responsible AI deployment.
This course is designed for technical professionals who want to confidently participate in AI conversations and decisions. Whether you're evaluating AI tools, designing systems that incorporate AI, or simply want to understand the technology transforming your industry, you'll gain the foundational knowledge to move forward with clarity and confidence.
The AI Journey: From Dartmouth to Deep Learning
Trace AI's evolution from its 1956 birth through multiple winters and summers to today's breakthrough moment. Understand the key paradigm shifts that made modern AI possible and why this time is different.
How Machines Actually Learn: Neural Networks Demystified
Peek inside the black box to understand how neural networks learn patterns from data. Explore the fundamental concepts of training, weights, and why more parameters often mean better performance.
The Language of AI: Tokens, Context, and Prompts
Master the fundamental unit of AI communication—the token—and learn how context windows shape what's possible. Understand how to craft effective prompts and why token economics matter.
Controlling AI Behavior: Temperature, Parameters, and Determinism
Learn to fine-tune AI outputs by controlling key parameters that govern creativity, consistency, and randomness. Discover how to make AI responses more predictable or more creative depending on your needs.
Model Types and Knowing What They Don't Know
Explore the landscape of AI models from instruction-tuned to reasoning models. Understand uncertainty quantification and why knowing when AI is uncertain is crucial for reliable systems.
Where AI Fails: Hallucinations, Limitations, and Knowledge Gaps
Confront AI's limitations head-on, from hallucinations to outdated knowledge and reasoning failures. Understanding where AI breaks down is essential for building reliable systems.
Retrieval-Augmented Generation: Grounding AI in Facts
Discover how RAG solves AI's knowledge limitations by combining retrieval systems with generation. Learn the architecture that powers AI systems that can access fresh, domain-specific information.
Responsible AI: Governance, Ethics, and Production Readiness
Build frameworks for deploying AI responsibly in production environments. Address governance, monitoring, cost management, and the human factors that determine success or failure.
8 sessions · 2 hours each · 16 hours total