@inproceedings{raptors2026-c063,
  title     = {{Semantic Condensation of High-Cardinality Time Series for LLM-Driven Observability}},
  author    = {Balasubramanian, Akila},
  booktitle = {Proceedings of the Raptors Conference},
  volume    = {1},
  year      = {2026},
  publisher = {Hackathon Raptors},
  address   = {London, United Kingdom},
  doi       = {10.68495/rc.2026.C063},
  url       = {https://doi.org/10.68495/rc.2026.C063},
  abstract  = {Large language models do not usually fail because they cannot reason. They fail because of the inputs they are given, and in observability that means raw, high-cardinality, high-volume time series data that carries very little signal per token. This paper shows how to turn that raw data into structured, high-signal input, and demonstrates the improvement in outcomes that follows from doing so. The underlying argument generalizes well beyond observability: in any system where the question is extracting signal, condensing the input matters more than enlarging the context window, and the instinct to supply more is usually the wrong response to a disappointing result. The paper treats input preparation as the engineering problem rather than the preliminary to it. Readers take away a different way of preparing data for a language model, oriented toward the signal the model actually needs rather than the volume it can technically accept.}
}