rcode-distillator

Lossless document compression for LLM context works by applying thematic compression rules and semantic splitting to preserve headings, named entities, decisions, and relationships while removing repetition and filler. The output is a dense markdown distillate ready for downstream model consumption.

Yes, you can distill mixed JSON, YAML, text, and markdown documentation sets into a single LLM-ready context file. The analyzer processes mixed documentation folders and groups content into compact markdown themes for downstream comparison, analysis, or archival condensation.

Semantic splitting breaks large source documentation sets into coherent section distillates when token budgets or document size require it. This allows the distillation process to handle large architecture notes, PRDs, or research reports without exceeding downstream model context limits.

Round-trip validation catches hallucinations in compressed LLM context by reconstructing and comparing distillates against original sources to identify gaps. It ensures the compression preserved every meaningful heading, named entity, constraint, decision, and relationship without losing critical information.

The best way to prepare architecture notes and PRDs for LLM analysis is to distill them into a lossless markdown context file using thematic compression. This removes unnecessary prose while preserving decisions, constraints, and relationships for dense context loading during downstream planning or review.