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When you generate questions in Question Studio, you are not chatting with a generic AI. You are handing work to an AI agent: a system that takes a goal, makes a plan, does the work in steps, checks its own output, and hands you a result to approve. This page explains what an agent is, walks through the loop it runs every time you generate, and translates the software terms you might hear into what they actually mean for you as a LearnTerms user.

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What “agent” actually means

A plain AI model answers one prompt at a time. An agent wraps a model in a repeatable process: it can break a job into stages, look things up, and judge its own results before showing them to you. In LearnTerms, that job is always the same: turn the source material you selected into solid, exam-style questions for a specific module. The agent is built to do that job well, which is why it cannot wander off-topic, chat freely, or pull in facts from outside your material. What it means for you: you get questions that stay close to your course content, not generic trivia. The trade-off is that the quality of what you get depends heavily on the quality of the source you give it.

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The agent loop, step by step

Every generation run moves through the same stages. You can watch them happen in the timeline panel while the agent works.
1

Plan

The agent first reads your selected topics and source pages and writes a short coverage plan: how many questions per topic, which pages back them up, what difficulty mix to aim for, and any risks it sees (thin coverage, overlap, possible duplicates). It does not write questions yet.
2

Draft

Using that plan, the agent drafts question candidates grounded only in your source pages. Each candidate is tied to one topic and one set of source pages.
3

Review

The agent then re-reads its own drafts against the source and grades each one: accept, revise, or reject. Weak, ambiguous, or unsupported questions get fixed or thrown out before you ever see them.
After the loop finishes, you get a clean set of candidates to inspect, keep, or discard. You always have the final say — the agent accelerates authoring, it does not replace your editorial judgment. What it means for you: because the agent plans and reviews on its own, the questions you see have already passed a first round of quality control. You are reviewing a curated draft, not raw output.

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Software terms, translated

Here is the vocabulary you may run into, and what each term means for your day-to-day use.

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Why the agent is deliberately limited

A few guardrails are built in on purpose:
  • It only reads what you select. The agent has no access outside your chosen document and page range.
  • It cannot cite the source out loud. Questions and rationales explain the clinical or factual reasoning directly — you will not see phrases like “according to the notes.”
  • It blocks near-duplicates. Questions too similar to ones already in the module are filtered out.
  • It respects usage limits. Generation is capped per user and across the service to keep things fair and stable.
What it means for you: these limits are why LearnTerms generation feels focused and exam-relevant instead of like a chatbot. Working with them — clean source, clear focus notes, smaller batches — gets you better questions faster.

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Getting the best results

  1. Feed it good source. Most “bad generation” is really a source-material problem. Clean your chunks first.
  2. Pick the matching model. Treat model choice as a relevance decision — see LearnTerms generation models.
  3. Use focus notes. They outrank the default plan, so use them to set angle, difficulty, and what to avoid.
  4. Generate smaller batches first. Review, learn what works, then scale up.
  5. Edit after insertion. Keep the strong questions, refine the rest — don’t accept blindly.