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Alex1980Alex
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evaluation-benchmark

Quantify search and RAG output quality using standardized retrieval metrics.

1|Updated Sep 20, 2025
Search Tags:#recall#rag#metrics#evaluation#benchmark#precision#ndcg
One-click install
npx skills add https://github.com/Alex1980Alex/1C-Enterprise_Framework --skill evaluation-benchmark
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluation-benchmark
Source: https://github.com/Alex1980Alex/1C-Enterprise_Framework/tree/main/.claude/skills/evaluation-benchmark
Command: npx skills add https://github.com/Alex1980Alex/1C-Enterprise_Framework --skill evaluation-benchmark

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Efficient and repeatable evaluation of search quality and RAG effectiveness across datasets, enabling objective comparisons and regression detection.

Core Features & Use Cases

  • Comprehensive metrics suite (precision, recall, NDCG, MRR) for retrieval quality.
  • RAG-focused evaluation with context relevance, grounding, and answer relevance scoring.
  • Automated pipelines to run benchmarks, collect results, and generate reports for multiple strategies (vector, BM25, hybrid).

Quick Start

Run an initial benchmark on your dataset to establish a baseline and compare strategies.

Frequently Asked Questions about evaluation-benchmark

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate RAG performance and search quality across different datasets?▼

You can evaluate RAG performance and search quality by applying standardized metrics like precision, recall, and NDCG across datasets using automated reporting pipelines to benchmark vector, BM25, and hybrid retrieval strategies.

What metrics are used for benchmarking retrieval quality in machine learning pipelines?▼

Benchmarking retrieval quality utilizes a comprehensive metrics suite including precision@k, recall, NDCG, and MRR to quantify ground-truth relevance and track progress in ML search pipelines.

Can I run regression testing on BM25 and vector retrieval strategies?▼

Yes, you can run regression testing on vector, BM25, and hybrid retrieval strategies. The evaluation tooling applies standardized metrics across datasets to enable repeatable benchmarking and detect search pipeline regressions.

How does RAG evaluation measure context relevance and answer grounding?▼

RAG evaluation measures context relevance, grounding, and answer relevance scoring to quantify the effectiveness of retrieval-augmented generation outputs against ground-truth relevance and ensure objective quality tracking.

What is the best way to compare hybrid and vector search strategies objectively?▼

The best way to compare hybrid and vector search strategies is by running automated benchmark pipelines that establish a baseline using standardized metrics like NDCG and precision across datasets to generate objective comparison reports.

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