> ## Documentation Index
> Fetch the complete documentation index at: https://docs.interpscout.org/llms.txt
> Use this file to discover all available pages before exploring further.
# Document Workshop
> Five stages from raw source to a mechanically accepted course glossary
Course production isn't one tool call. It's a five-stage pipeline where mechanical tools do the deterministic parts and the agent's judgment is reserved for the one part that genuinely needs it: deciding which terms in a document are worth an interpreter's preparation time.
## The five stages
A tool fetches the source, follows pagination if the source is paginated, cleans and aligns it into Chinese/English paragraph pairs, and runs an identity check to confirm the fetched text is actually the document the teacher asked for — not a different document with a similar title. This stage writes a skeleton straight to the database (paragraphs, no terms yet) and writes the aligned text out to a set of sandbox files. It does not extract any terminology.
The agent reads the staged document window by window — never the whole thing at once — and curates a candidate glossary as it goes, writing candidates to a sandbox file after every window rather than holding them in memory. A second pass rechecks anything it flagged as uncertain and greps for word-family members it may have missed the first time around. Uncertain official renderings get a web search, not a guess.
The finished candidate glossary — plus a manifest of the document and a sample of the source text — is handed to a separate reviewer sub-agent with its own fresh context and no access to the producing agent's history. Its explicit job is to falsify, not to approve: it flags fabricated or misaligned pairs, everyday words below an interpreter's difficulty bar, whole section headings copied in as if they were terms, plausible-but-not-official renderings, and — just as important — terms it can see in the sample that the glossary missed. The producing agent revises against that verdict before moving on.
The revised glossary goes through a save tool that runs it past a stack of deterministic gates — not a second opinion from another model. A term that fails any gate is rejected with a specific, actionable reason; if too many terms fail, or too few survive relative to the document's length, the whole submission is rejected and sent back for another curation pass inside the same agent session.
Once the gates pass, the course is marked complete in the same transaction that saves its content — there's no separate "finalize" step that can silently fall out of sync with what was actually saved.
## What the mechanical gates actually check
The submission gates aren't a vague quality score. Each one checks something specific and reproducible:
* **Stopword and boilerplate rejection** — site names and navigation text that occasionally get scraped in as if they were terms.
* **A length cap** — nothing that looks like a whole sentence or a filename gets accepted as a "term."
* **A ratio band between the Chinese and English sides** of a pair, to catch fragment mismatches (for example, an alignment of 武汉保卫战 with only "Wuhan" — a fragment, not a translation).
* **Three-tier literal verification** — every submitted term has to actually be locatable in the source text the course was built from; a term the agent can't be found verbatim in the document is rejected as fabricated or paraphrased, not silently kept.
* **Title-row rejection** — a term that turns out to be sitting inside a heading rather than body text is rejected, even if it passed every other check.
* **A density floor and a rejection-ratio ceiling** — if the surviving glossary is too thin relative to the document's length, or too much of what was submitted got rejected, the whole batch bounces back rather than shipping a sparse or low-quality result.
* **Alignment granularity assertions** — for documents that needed manual paragraph alignment, a single aligned pair can't span more than roughly 700 Chinese characters, and the aligned pairs have to cover a real majority of the source's paragraphs, so a "700-word chapter crammed into one giant pair" alignment can't sneak through.
A rejection here isn't a failure state for the task — it's an instruction. The agent reads the specific reasons, fixes the glossary inside the same session, and resubmits. Only a source that turns out to be unusable (not actually bilingual, not the document that was asked for, an excerpt rather than the full text) ends production outright.
The full philosophy behind "gates check, prompts don't guarantee" is in [Truth Layer & Mechanical Invariants](/internals/truth-layer).