trueskill-rank

You can rank text items by quality using a TrueSkill-based scoring pipeline that dispatches overlapping subsets to LLM workers, outputting final rankings in JSON. It operates at scale by generating overlapping subsets for evaluation.

TrueSkill ranking is a scoring mechanism that uses LLM-as-judge to evaluate text items. It operates at scale by dispatching overlapping subsets to Codex Spark workers, supporting batch and pairwise modes with swappable rubrics.

Yes, running an LLM-based text ranking pipeline requires Python 3.11+ and the trueskill library. You must install the dependency and prepare input items with id and text fields before executing the full pipeline.

Yes, you can use custom rubrics for domain-agnostic text evaluation. The pipeline supports swappable rubrics, allowing you to define specific quality criteria for ranking your collection of text items.

The best way to scale LLM evaluation is using a TrueSkill pipeline that generates overlapping subsets and dispatches them to Codex Spark workers. This batch and pairwise approach supports scalable, deterministic quality ranking.

The TrueSkill ranking pipeline outputs rankings in JSON format. This structured output allows for seamless integration into downstream automation workflows after the LLM evaluates the text items.