Every novel goes through a multi-stage pipeline that pairs AI translation with human editorial review — curated glossaries, arc-level context, and a quality gate before anything is published.
Source chapters are fetched and parsed into clean, structured text — dialogue, paragraph breaks, and narrative formatting preserved exactly as written. The raw material for everything that follows.
Before a single line is translated, the source text runs through two LLM passes dedicated solely to term discovery. Stage 1 casts a wide net across character names, cultivation ranks, martial arts techniques, locations, and faction names. Stage 2 refines the translations and resolves ambiguities in how terms relate to each other.
Every extracted term is reviewed by a human editor: mistranslations corrected, aliases added, context notes written, and important terms locked so they can never drift across hundreds of chapters. This step is what separates a curated glossary from an extraction dump.
Chapters are translated with the full curated glossary loaded alongside chapter-level summaries and arc-level summaries built from everything that came before. The model knows the narrative context — not just the current chapter — so character voice and story continuity hold across long serials.
An automated QA scan checks every chapter: empty output, title mismatches, extreme length ratios, duplicate content, residual Chinese characters. Chapters that fail are rerun before any human sees them. Chapters that pass go to editorial review — nothing is published without sign-off.
Key terms — main characters, cultivation ranks, named techniques — are verified and locked by a human editor, not just auto-generated. Consistent naming across hundreds of chapters isn't luck; it's enforced at the source.
The translation model doesn't just see the current chapter. It receives a rolling window of chapter summaries and arc summaries, so plot threads don't drop and character relationships stay coherent across long arcs.
Every chapter passes a mechanical scan before reaching a human. Issues found by the scanner trigger targeted reruns before review. Editors see output that has already passed automated quality gates — not a first draft.
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