Artificial Intelligence
Artificial intelligence covers the concepts behind modern AI systems: how models learn, how large language models work, and how to build grounded, cited applications on top of them responsibly.
Who this is for: Developers who want to understand and build with modern AI systems.
Suggested starting point: AI Agents
Technologies (11)
AI Agents
LLMs that decide which tools to call in a loop to accomplish a goal.
Artificial Intelligence
Systems that perform tasks normally requiring human intelligence.
Deep Learning Foundations
Machine learning using multi-layer neural networks.
Embeddings
Numeric vectors representing meaning, enabling similarity search.
Generative AI
Models that generate new text, images, or other content.
Large Language Models
Transformer-based models trained on huge text corpora to generate and understand language.
Machine Learning
Algorithms that learn patterns from data rather than following fixed rules.
MLOps
Operating machine learning systems reliably in production.
Prompt Engineering
Structuring instructions and context to reliably get useful output from a model.
Responsible AI
Building AI systems that are safe, fair, and honest about their limitations.
Retrieval-Augmented Generation
Grounding LLM answers in retrieved, real source content instead of memory alone.
Related learning roadmaps
- AI and Generative AI EngineerFrom neural network intuition to building grounded, cited retrieval-augmented AI applications.
- Data ScientistPython plus the numeric computing stack, for building models and analyzing complex data.
- Zero to AI BuilderStart with zero coding experience and build up to a real, cited retrieval-augmented AI application.