gemini-chunked-generation

Generating large documents without hitting context limits requires a three-phase pipeline that breaks source sets into smaller batches for extraction, compiles the extracted data, and synthesizes the final output to maintain quality across 3000+ lines.

Repetitive templates and filler occur during large-document synthesis because the model runs out of context. A chunked generation approach processes sources in smaller batches to create dense markdown fichas, preventing context collapse and filler output.

The best way to convert large source folders into reverse-engineering blueprints is a staged three-phase process: batch extraction from sources, centralized compilation of dense fichas, and context-aware synthesis to produce the final derived document.

Creating a 3000-line PRD from dozens of source files in a single prompt often fails due to context limits. You need staged document generation with batch sizing and output validation to process source sets sequentially and compile them effectively.

Batch extraction for multi-file documentation workflows works by breaking large source sets into smaller chunks, processing each batch independently to produce dense markdown fichas, and then compiling those fichas into a centralized final deliverable.

You should use a chunked generation pipeline when you need to turn dozens or hundreds of source files into a massive document and want to avoid losing synthesis quality, context collapse, or appending raw source material as filler.