AI Engineering Terms, Explained
The building blocks of modern AI systems, one at a time. Explained concretely, by the people who build them.
AI systems are made of a handful of building blocks that keep coming back: Token, Embeddings, Context Window, RAG, Evals. Understanding these terms is understanding why an AI project holds up or falls over.
This series explains them one by one. Each post takes a single term, starts from a concrete example rather than a definition, and stays easy to read. For more depth, every post has a separate, skippable technical box with the details.
The Machine
How a model runs, what it costs, and where the memory runs out first.
- Token live
- Context Window soon
- Embedding soon
- Attention soon
- KV Cache soon
- Quantization soon
The Harness
How a text generator turns into a system you can hand a task to.
- Structured Output soon
- Tool Use soon
- Agents soon
- RAG soon
The Discipline
How to measure, observe, and safely run AI systems for many tenants.
- Evals soon
- Observability soon
- Prompt Injection soon
- Multi-Tenancy soon
The Judgment
Which tool for which problem. The decisions that make a system.
- Fine-tuning vs. RAG soon
- Build vs. Buy soon
The series grows post by post. Terms without a link are in the works.
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