The Fundamentals
Artificial Intelligence (AI)
Computer systems that perform tasks requiring human intelligence — learning, reasoning, problem-solving, understanding language.
Machine Learning (ML)
Systems that learn from data rather than following explicit rules. Improves as it processes more data.
Deep Learning
ML using neural networks with multiple layers. Powers image recognition, NLP, and generative AI.
Generative AI (GenAI)
AI that creates new content — text, images, code, music, video. Examples: ChatGPT, Claude, Midjourney.
Large Language Model (LLM)
Technology powering text-based GenAI. Trained on massive text datasets to understand and generate human language.
Key Concepts
Prompt
Your input/instruction to an AI system. Better prompts = better outputs.
Hallucination
When AI generates confident but incorrect information. Always verify!
Context Window
How much information an AI can process at once.
Temperature
Controls output randomness. Lower = predictable, Higher = creative.
Implementation Terms
API
How software systems communicate. AI APIs let you integrate AI into your tools.
Automation vs Augmentation
Automation = AI does the task. Augmentation = AI assists humans who make final decisions.
Center of Excellence (CoE)
Team guiding AI adoption. More important as AI usage scales.
AI Governance
Policies ensuring responsible AI use. Critical for regulated industries.
Ready to Learn More?
Explore more AI resources or take our readiness quiz.