This blog was written in collaboration with Fan Bu, Jason Mackay, Borya Sobolev, Dev Khanolkar, Ali Dabir, Puneet Kamal, Li Zhang, and Lei Jin.

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ACE (Analytics Context Engineering) is a framework that optimizes how machine data (logs, metrics, telemetry) is presented to LLMs by transforming raw JSON payloads into hybrid row-oriented and columnar views. Instead of feeding verbose nested data directly into prompts, ACE stores full-fidelity data in a datastore and uses simple SQL queries with a virtual file system to generate token-efficient representations. Benchmarks show 20-90% token reduction, lower latency, and improved answer accuracy across networking observability tasks like anomaly detection, slot filling, and time-series visualization.

Analytics Context Engineering for LLM

“Everything is a file”; some are databases