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  2. Search and Match

Search and Match

  • Can every output be reviewed or audited if needed?
  • Does RChilli maintain reports, dashboards, and alerts for performance degradation?
  • How are test results recorded and monitored?
  • What safety and risk controls are in place to prevent harmful or incorrect outputs?
  • What internal performance benchmarks and metrics does RChilli track?
  • How does RChilli measure matching accuracy?
  • How does RChilli measure Parsing Accuracy?
  • Does RChilli provide Traceable Reasoning or Logs for Outputs?
  • How does RChilli ensure model explainability and transparency?
  • How often are bias and model quality tests conducted?
  • Does RChilli use fairness metrics? If yes, which ones?
  • What Datasets are used for Bias Evaluation?
  • How does RChilli test models for bias across gender, ethnicity, location, age, or other sensitive attributes?
  • Which sensitive attributes are excluded or controlled to reduce bias?
  • How does RChilli approach fairness and bias prevention in its AI systems?
  • How much time does it take to perform a search query on an Index per Job?
  • What technology is used for identifying relevant and adjacent skills, and what is the baseline for skill extraction and matching?
  • How does the Search and Match mechanism work, and what is the underlying basis of its operation?
  • Is there a way to delete the indexed documents with the sub user ID?
  • How to customize the Dynamic Weightage ?
  • What are the Skill types that RChilli have or supports?
  • Is there any limit to index the document?
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