Learn AI Concepts
A practical guide to modern AI concepts — how to use them, not the math behind them. Each page explains one concept well enough that you can actually build with it: what it is, why it exists, when to reach for it, and when not to.
- Learn — every concept on the site, grouped by category.
- Compare — head-to-head answers for concepts that are easy to confuse.
- Interview questions — practice questions grouped by topic, linked back to the full explanation.
35 concept pages across 13 categories so far, plus 20 comparison pages and practice interview questions for major topics — growing every week.
Browse by category
- AI Essentials — The basic building blocks worth understanding before the rest of this site's agent, tool, and retrieval concepts make full sense (6 concepts).
- RAG — Retrieval-augmented generation grounds a model's answers in your own data at query time, instead of relying only on what it learned during training (6 concepts).
- AI Agents — An AI agent decides its own next action instead of following a fixed script. What that means in practice, and what changes once more than one agent is involved (5 concepts).
- AI Safety — The practical end of AI safety: how applications built on models actually get attacked, and how to limit the damage when something gets through (3 concepts).
- Prompting — How to actually instruct a model, what does and doesn't change the way it responds, and what belongs in its context at all (3 concepts).
- Tools — The mechanisms that let a model do something — take an action, look something up — instead of only answering in text (3 concepts).
- AI Evaluation — How to tell whether an AI system is actually working, rather than just looking like it is, and how to keep telling once anything about it changes (2 concepts).
- MCP — MCP standardizes how an AI application connects to external tools and data, instead of every pairing needing its own custom integration code (2 concepts).
- Agent Skills — A skill packages instructions for one kind of task, kept out of an agent's prompt until that task actually comes up (1 concept).
- Context Engineering — What information a model actually sees for a given task, and how to decide what belongs in there and what doesn't (1 concept).
- Memory — What an AI system should deliberately remember between sessions, and what should stay temporary context instead (1 concept).
- Production AI — Running AI systems for real, once actual users depend on them — cost, latency, monitoring, and what breaks at scale (1 concept).
- Subagents — A subagent is the same kind of system as an ordinary agent — the difference is who's calling it, and what that changes about how you'd design it (1 concept).