The words that run AI-era engineering.

An AI agent is an LLM-powered system that pursues a goal autonomously — planning steps, calling tools, and iterating on results until the task is done.

COSTAR is a prompt-writing checklist — Context, Objective, Style, Tone, Audience, Response format — that turns vague requests into complete, reliable prompts.

An embedding is a list of numbers (a vector) that represents the meaning of text, so that semantically similar texts end up numerically close together.

A hallucination is when an AI model states false information with confidence — fabricating facts, citations, or code that looks plausible but is wrong.

Hybrid search combines keyword search (like BM25) with vector search in one query, then merges the results — catching both exact matches and semantic matches.

An incident postmortem is a blameless written analysis of an outage — timeline, root causes, impact, action items — to prevent recurrence, not assign fault.

LLM evals are systematic tests that measure an AI system's quality — scoring outputs against defined criteria so changes are verified with data, not vibes.

Mastery learning is an approach where students must demonstrate mastery of each topic — typically 90%+ — before advancing; time varies, learning does not.

Prompt engineering is the practice of designing model inputs — instructions, context, examples, and output format — to get reliable results from an LLM.

Reciprocal rank fusion merges multiple ranked search result lists by scoring each item as 1/(k + rank), summed across lists — no score normalization needed.

RAG is a technique where an AI system retrieves relevant documents first, then generates an answer grounded in them — cutting hallucination sharply.

SM-2 is the classic spaced-repetition scheduling algorithm: after each self-graded review, it updates an ease factor and computes the next interval.

Spaced repetition is a learning technique that schedules reviews at increasing intervals, timed just before you forget — producing durable memory.

A T-shaped engineer combines broad working knowledge across many domains (the horizontal bar) with deep expertise in one specialization (the vertical stem).

A token is the smallest unit of text a language model reads and writes — usually a word fragment of roughly 3-4 characters in English.