Commandments of LLM Use (www.mostlylucid.net)

A new implementation of the Graph Retrieval-Augmented Generation (GraphRAG) has been announced, focusing on an offline-first approach that minimizes LLM usage while effectively handling technical documentation. Unlike Microsoft's version that relies heavily on multiple LLM calls for entity extraction and relationship building, this streamlined method enables a locally run system that uses DuckDB for storing relations and BERT embeddings for entity detection without incurring high costs. The architecture allows for efficient retrieval queries, using statistical methods instead of complex LLM passes, ultimately making it far cheaper (approximately $0.10 compared to $5-10) for indexing structured technical content. This development is significant for the AI/ML community as it addresses the challenge of running advanced models in a cost-effective and practical manner, particularly for technical documentation which often has specific structures like headings and code snippets. The use of heuristics and statistical techniques ensures that while the results may sacrifice some recall, they gain in predictability and auditability—qualities essential for many structured datasets. The performance improvements highlight the potential for AI systems that are not only intelligent but also resource-efficient, paving the way for wider adoption in industries reliant on complex documentation processing.