AI Fundamentals 101
Eight beginner lessons on artificial intelligence — explained in plain language for people who use or encounter AI without a technical background.
AI is increasingly present in workplaces, public services, and everyday tools. This path explains what AI actually is, how the main types of AI systems work, and how to use AI tools thoughtfully and responsibly. No technical background required.
- Difficulty: Beginner
- 8 lessons reading
- No prior knowledge required
- What artificial intelligence is and what it is not
- How machine learning differs from traditional software
- What generative AI produces and why verification matters
- How large language models work at a beginner level
- How to write effective prompts and review AI output
- Why AI systems make errors and what hallucination means
- Privacy, bias, and responsible AI principles
- How to decide when AI is and is not the right tool
These paths are not required, but provide useful context:
- Data 101 — understanding data fundamentals helps when learning how AI systems are trained
- Data Best Practices 101 — data quality principles apply directly to AI training data
- Privacy 101 — privacy concepts are central to responsible AI use
What Is Artificial Intelligence?
A plain-language introduction to AI — what it is, what it is not, and how it differs from ordinary software, automation, and search.
Read Lesson →Machine Learning Explained
How machine learning works, how models learn from data, and why data quality matters for reliable results.
Read Lesson →Generative AI
What generative AI is, what it can produce, and why generated output requires verification before use.
Read Lesson →Large Language Models
What LLMs are, how they work at a beginner level, and why they differ from search engines and databases.
Read Lesson →Prompting AI Systems
How to write clear prompts, provide useful context, specify intended output, and review AI-generated results responsibly.
Read Lesson →AI Errors, Limitations, and Hallucinations
Why AI systems produce incorrect output, what hallucination means, and how to apply appropriate verification.
Read Lesson →Privacy, Bias, and Responsible AI
Personal information, bias, fairness, transparency, accountability, and responsible AI principles for organizations.
Read Lesson →Using AI Thoughtfully
How to identify a real problem before choosing AI, evaluate whether simpler tools suffice, and use AI with appropriate oversight.
Read Lesson →- Software for Data Work 101 — understand the software landscape that AI tools fit into
- Data Governance 101 — governance principles apply directly to AI oversight
- Metadata 101 — metadata is essential for documenting AI systems and their data
- AI Glossary — plain-language definitions for AI terminology