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RAG and context

RAG Retrieval Score

Compute precision@k and recall@k scores for your RAG retrieval pipeline. Measure how many relevant documents are retrieved at each rank position and identify systematic retrieval failures that reduce answer quality.

Run RAG Retrieval Score Interactively 100% client-side execution in your browser. Zero data retention.
Open Interactive Tool

What Problem RAG Retrieval Score Solves

Production AI deployments require predictable token economics, rigorous parameter verification, and deterministic schema handling. RAG Retrieval Score isolates parameters client-side before payloads hit third-party foundation model APIs.

Primary Production Use Cases

  • Benchmark retrieval quality across embedding models
  • Measure impact of chunking changes on retrieval

Security & Data Privacy Considerations

All computations execute locally in browser memory. No prompt payloads, test inputs, or secret credentials are ever retained or stored on central servers.

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