Semantic Layer Induction from Raw Telemetry via Hierarchical LLM and RAG Abstraction

The paper introduces an end‑to‑end framework that automatically builds a business semantic layer from raw application logs. It uses a two‑stage abstraction: first, large language models identify high‑level business features with industry knowledge, and second, a structured pipeline refines data, retrieves relevant information, filters, clusters semantically, and assigns canonical names. Evaluation on production‑scale telemetry shows significant gains in semantic quality, noise reduction, and maintenance effort, with a 0.87 Cohen’s kappa in an LLM‑as‑Judge assessment.

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• llms
• rag