AI Engineering Research Note2025Canonical long-form →

RAG Recall Eval

RAG service that proves its own retrieval : recall@3 = 0.886, MRR@3 = 0.805 with offline stdlib TF-IDF retriever.

AI EngineeringPublished Sat Nov 15 2025 00:00:00 GMT+0000 (Coordinated Universal Time)

EVAL-FIRST31 GATESNDA-CLEANPUBLIC DATAALPHASIGNAL > NOISEREPRODUCIBLENOTEBOOK-COMMITTEDCITABLEBIBTEX + DOI

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A copy-pasteable BibTeX entry. The canonical long-form with figures, code, and full methodology lives at the URL in the entry. please link there, not here, when citing in a paper or a thread.

@techreport{macion2025ai01ragrecall,
  author       = {Macion, Christian T.},
  title        = {RAG Recall Eval},
  institution  = {Independent research},
  year         = {2025},
  date         = {2025-11-15},
  note         = {Public-data reproducible. Canonical long-form: https://christianmacion-portfolio.pages.dev/projects/ai/01-rag-recall/},
  url          = {https://christianmacion-portfolio.pages.dev/projects/ai/01-rag-recall/}
}

Canonical surface

The full research note. methodology, code, gates run, and reproduced metrics. lives on the projects index.

  • Canonical URL/projects/ai/01-rag-recall ↗
  • Headline metric(s)
    • 0.886recall@3
    • 0.805MRR@3
    • 1.00Mean faithfulness
    • 0 / 35Hallucination flags
  • Tags
    • rag
    • evaluation
    • retrieval
    • faithfulness
    • stdlib

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