What question did this study set out to answer?

The aim is to explore efficient knowledge extraction methods using LLMs without traditional databases or structures.

May 11, 2026Open Access

Knowledge Compile: Incremental LLM-Powered Knowledge Extraction Without Databases, Embeddings, or Graphs

Puntos clave

The aim is to explore efficient knowledge extraction methods using LLMs without traditional databases or structures.
Compile 192 ancient texts over 1,800 years with LLMs for knowledge extraction.
Implement a system that allows searching through compiled knowledge using grep.
Reduce token usage by 18x to optimize performance.
Achieved a total compilation cost of $36 for extracting knowledge across 192 texts.
Enabled instant search capabilities for extracted concepts and FAQs.
Demonstrated efficiency through notable token reduction without requiring database infrastructure.

Resumen

Compile once, grep forever - 192 ancient texts across 1, 800 years, 18x token reduction, 36 total cost, zero infrastructure. LLMs are better curators than retrievers. The companion to grep-is-all-you-need (#4): instead of using LLMs at query time to retrieve, use them at compile time to extract concepts and FAQs that grep can search instantly. Note (2026-05-09): This version bundles English + 中文 in a single PDF (English first, then Chinese), generated directly from the canonical Markdown source files.

Leer artículo completoexternamente

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo