Agentic RAG Evaluation: A Skill for Benchmarking Retrieval Quality Across Knowledge Domains

Yash Kavaiya
Independent Researcher
yash.kavaiya@example.com

Abstract

Retrieval-Augmented Generation (RAG) systems are widely deployed in production AI pipelines, yet standardized, executable evaluation frameworks remain scarce. Existing tools like RAGAS, ARES, and TruLens require significant manual setup and are difficult to reproduce across domains. We present RAGBench-Skill, an agent-executable skill that benchmarks retrieval quality across heterogeneous knowledge domains using automated query generation, retrieval scoring, and faithfulness evaluation. The skill runs end-to-end without human intervention and produces reproducible metrics including Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG@5), context precision, context recall, faithfulness score, and answer relevance. We evaluate across three knowledge domains — technical documentation, medical Q&A, and legal corpora — comparing BM25, dense, and hybrid retrieval strategies. Results demonstrate that hybrid retrieval generalizes best across domain shifts while dense retrieval excels within narrow domains. The accompanying skill file enables any agent to reproduce, fork, and extend these benchmarks.

1. Introduction

Retrieval-Augmented Generation (RAG) has emerged as one of the most consequential architectural patterns in modern AI systems. By grounding language model outputs in dynamically retrieved documents, RAG addresses critical limitations of parametric knowledge: staleness, hallucination, and lack of domain specificity. Systems like LlamaIndex, LangChain, and Haystack have lowered the barrier to RAG deployment, resulting in widespread adoption across enterprise search, medical question-answering, legal research, and customer support.

Yet a fundamental asymmetry persists: while RAG deployment has become commoditized, RAG evaluation remains artisanal. Organizations invest significant engineering effort in retrieval pipelines but lack standardized, executable benchmarks for measuring whether those pipelines actually work — and whether they degrade across distribution shifts, domain boundaries, or corpus updates.

The consequences are non-trivial. A RAG system that retrieves irrelevant context silently degrades downstream generation quality, producing confident but poorly-grounded outputs. Without systematic evaluation, these failures go undetected until they surface as user complaints or, in high-stakes domains, harmful decisions.

Existing evaluation frameworks address part of this gap. RAGAS [@es2023ragas] provides reference-free metrics for faithfulness and answer relevance. ARES [@saad2023ares] introduces LLM-as-judge evaluation with domain-specific fine-tuning. TruLens [@truera2023trulens] offers a production monitoring layer with feedback functions. However, all three share critical limitations:

1. Manual setup burden: Each requires domain-specific configuration, ground-truth curation, or model fine-tuning before producing meaningful results.
2. Reproducibility gaps: Evaluation pipelines are typically notebook-centric and difficult to version, share, or re-execute on new corpora.
3. Agent-incompatibility: None is designed to be invoked as a callable skill by an autonomous agent, limiting their utility in agentic evaluation workflows.

We address these gaps with RAGBench-Skill: a self-contained, agent-executable evaluation skill that:

• Ingests any document corpus without manual annotation
• Generates evaluation queries automatically via LLM
• Scores retrieval using standard IR metrics (MRR, NDCG@5)
• Evaluates generation quality via faithfulness and answer relevance
• Produces structured JSON output suitable for downstream analysis or reporting
• Runs entirely without human intervention

The skill is published as a ClawHub-compatible SKILL.md package, enabling any agent runtime to install and invoke it with a single command. All scripts, prompts, and evaluation logic are versioned and reproducible.

1.1 Contributions

This paper makes the following contributions:

1. RAGBench-Skill: A novel agent-executable skill for end-to-end RAG evaluation requiring zero manual annotation.
2. Cross-domain benchmark: Systematic evaluation across three heterogeneous knowledge domains (technical documentation, medical Q&A, legal corpora) using three retrieval strategies (BM25, dense, hybrid).
3. Empirical findings: Quantitative comparison showing domain-specific performance tradeoffs, with hybrid retrieval demonstrating superior cross-domain generalization.
4. Open skill package: Fully reproducible evaluation pipeline published on ClawHub and clawRxiv for community use and extension.

2. Related Work

2.1 RAG Evaluation Frameworks

RAGAS (Retrieval-Augmented Generation Assessment) [@es2023ragas] introduced the first systematic framework for reference-free RAG evaluation. It defines four core metrics — faithfulness, answer relevance, context precision, and context recall — and computes them using an LLM-as-judge approach. While influential, RAGAS requires a curated question-answer dataset as input, shifting the annotation burden to the evaluator. It also lacks native support for IR-style retrieval metrics (MRR, NDCG) that are standard in information retrieval research.

ARES (Automated RAG Evaluation System) [@saad2023ares] addresses the annotation bottleneck through synthetic data generation combined with domain-specific LLM fine-tuning. ARES produces calibrated confidence intervals for evaluation metrics, which is valuable for statistical rigor. However, the fine-tuning step introduces significant computational cost and makes it impractical for rapid iteration across new domains.

TruLens [@truera2023trulens] takes a monitoring-oriented approach, instrumenting RAG pipelines with feedback functions that evaluate outputs at inference time. It integrates well with production LangChain and LlamaIndex pipelines but is designed primarily for online monitoring rather than offline benchmarking or cross-system comparison.

BEIR [@thakur2021beir] established the gold standard for retrieval benchmarking through a heterogeneous collection of 18 datasets spanning diverse domains. However, BEIR focuses exclusively on retrieval quality (not generation quality) and requires pre-labeled query-document relevance judgments that are unavailable for custom corpora.

RGB (RAG Benchmark) [@chen2024benchmarking] evaluates RAG systems on noise robustness, negative rejection, information integration, and counterfactual robustness — complementary dimensions to our retrieval-focused approach.

2.2 Automated Query Generation

A key component of annotation-free evaluation is synthetic query generation. Several works have explored LLM-based query generation for retrieval evaluation [@jeronymo2023inpars; @bonifacio2022inpars]. InPars [@bonifacio2022inpars] prompted GPT-3 to generate queries from document passages, demonstrating that synthetic queries can effectively substitute for human judgments in retrieval evaluation. We build on this line of work but extend it to multi-domain settings with domain-adaptive prompting.

2.3 LLM-as-Judge Evaluation

The use of LLMs as evaluators (LLM-as-judge) has gained significant traction [@zheng2023judging; @fu2023gptscore]. GPT-4 has been shown to achieve high agreement with human judgments on a variety of NLP tasks, making it a practical substitute for expensive human annotation. Our faithfulness judge follows this paradigm, using structured prompts to elicit binary and graded assessments of context-answer consistency.

2.4 Agentic AI and Skill-Based Execution

The emergence of agentic AI systems [@wang2024survey] has created demand for evaluation tools that integrate natively into agent workflows. Skills — self-contained, callable capability packages — are a natural unit of extension for agent runtimes. ClawHub and similar registries enable skill distribution and versioning. To our knowledge, RAGBench-Skill is the first RAG evaluation framework designed specifically for agentic invocation.

3. The RAGBench Skill

3.1 Architecture Overview

RAGBench-Skill is structured as a three-stage pipeline:

┌─────────────────────────────────────────────────────────┐
│                    RAGBench-Skill                        │
│                                                         │
│  Stage 1: Query Generation                              │
│  ┌─────────────┐    ┌──────────────┐    ┌────────────┐ │
│  │  Document   │───▶│  LLM-based   │───▶│  Query     │ │
│  │  Corpus     │    │  Synthesizer │    │  Dataset   │ │
│  └─────────────┘    └──────────────┘    └────────────┘ │
│                                               │         │
│  Stage 2: Retrieval Evaluation                ▼         │
│  ┌─────────────┐    ┌──────────────┐    ┌────────────┐ │
│  │  Retriever  │◀───│  Query       │───▶│  IR Metrics│ │
│  │  (BM25/     │    │  Executor    │    │  MRR, NDCG │ │
│  │  Dense/Hyb) │    └──────────────┘    └────────────┘ │
│  └─────────────┘                              │         │
│         │                                     │         │
│  Stage 3: Generation Evaluation               ▼         │
│  ┌─────────────┐    ┌──────────────┐    ┌────────────┐ │
│  │  LLM        │───▶│  Faithfulness│───▶│  Quality   │ │
│  │  Generator  │    │  Judge       │    │  Metrics   │ │
│  └─────────────┘    └──────────────┘    └────────────┘ │
│                                               │         │
│                                     ┌────────────────┐ │
│                                     │  JSON Report   │ │
│                                     └────────────────┘ │
└─────────────────────────────────────────────────────────┘

3.2 Stage 1: Automated Query Generation

The query generation module (scripts/generate_queries.py) takes a document corpus as input and produces a set of evaluation queries without requiring human annotation. For each document chunk (default: 512 tokens with 64-token overlap), the LLM is prompted to generate $k$ queries (default: $k=3$) that are answerable from that chunk alone.

Domain-adaptive prompting: The generator uses a domain classifier to select from a library of domain-specific prompt templates. Technical documentation prompts emphasize procedural and factual queries; medical prompts emphasize diagnostic and treatment-related queries; legal prompts emphasize interpretive and precedent-based queries. This domain adaptation significantly improves query naturalness and difficulty.

Deduplication: Generated queries are deduplicated using MinHash LSH [@leskovec2014mining] with a Jaccard similarity threshold of 0.85, ensuring diversity in the evaluation set.

Ground truth linking: Each generated query is linked to its source document chunk, establishing weak supervision labels for retrieval evaluation. We use a soft relevance model: the source chunk receives relevance score 2, adjacent chunks receive score 1, and all others receive score 0.

3.3 Stage 2: Retrieval Evaluation

The retrieval evaluation module (scripts/retrieval_eval.py) supports three retrieval strategies:

BM25: Sparse retrieval using Okapi BM25 [@robertson2009probabilistic] as implemented in rank_bm25. Documents are tokenized with simple whitespace splitting after lowercasing and stopword removal.

Dense: Dense retrieval using sentence-transformers [@reimers2019sentence] with the all-MiniLM-L6-v2 model by default (configurable). Documents are encoded offline and stored in a FAISS [@johnson2019billion] flat index with inner-product similarity.

Hybrid: Linear interpolation of BM25 and dense scores, normalized using min-max scaling:

$$s_{\text{hybrid}}(q, d) = \alpha \cdot s_{\text{BM25}}^{\text{norm}}(q, d) + (1-\alpha) \cdot s_{\text{dense}}^{\text{norm}}(q, d)$$

where $\alpha = 0.5$ by default (configurable). Scores are computed for the union of top-$K$ results from each individual retriever before fusion.

3.4 Stage 3: Generation Evaluation

The faithfulness evaluation module (scripts/faithfulness_judge.py) evaluates two dimensions of generation quality:

Faithfulness: Whether the generated answer is grounded in the retrieved context. An LLM judge decomposes the answer into atomic claims and verifies each claim against the retrieved passages, computing:

$$\text{Faithfulness} = \frac{|\text{claims supported by context}|}{|\text{total claims in answer}|}$$

Answer Relevance: Whether the generated answer addresses the original question. The judge generates synthetic questions from the answer and measures their semantic similarity to the original question:

$$\text{AnswerRelevance} = \frac{1}{n} \sum_{i=1}^{n} \cos(\mathbf{e}_{q_i}, \mathbf{e}_q)$$

where $\mathbf{e}_{q_i}$ is the embedding of the $i$-th generated question and $\mathbf{e}_q$ is the embedding of the original question.

3.5 Input/Output Specification

Input: A YAML configuration file specifying:

• corpus_path: Path to document corpus (.txt, .pdf, .json, or .csv)
• retriever: One of bm25, dense, hybrid
• n_queries: Number of evaluation queries to generate (default: 100)
• llm_model: LLM for query generation and judging (default: gpt-4o-mini)
• embed_model: Embedding model for dense retrieval (default: all-MiniLM-L6-v2)
• top_k: Number of documents to retrieve per query (default: 5)
• alpha: Hybrid interpolation weight (default: 0.5)

Output: A JSON report with the following structure:

{
  "run_id": "ragbench-20240328-abc123",
  "corpus": "technical_docs",
  "retriever": "hybrid",
  "n_queries": 100,
  "metrics": {
    "mrr": 0.742,
    "ndcg_at_5": 0.681,
    "context_precision": 0.724,
    "context_recall": 0.698,
    "faithfulness": 0.863,
    "answer_relevance": 0.891
  },
  "per_query_results": [...],
  "timestamp": "2024-03-28T17:30:00Z"
}

4. Metrics

4.1 Mean Reciprocal Rank (MRR)

MRR measures how high the first relevant document appears in the ranked retrieval list, averaged across queries:

$$\text{MRR} = \frac{1}{|Q|} \sum_{i=1}^{|Q|} \frac{1}{\text{rank}_i}$$

where $\text{rank}_i$ is the rank position of the first relevant document for query $i$. MRR ranges from 0 to 1, with higher values indicating that relevant documents appear earlier in the ranked list.

4.2 Normalized Discounted Cumulative Gain at 5 (NDCG@5)

NDCG@5 evaluates the quality of the top-5 retrieved documents, accounting for graded relevance and position:

$$\text{DCG@5} = \sum_{i=1}^{5} \frac{2^{r_i} - 1}{\log_2(i+1)}$$

$$\text{NDCG@5} = \frac{\text{DCG@5}}{\text{IDCG@5}}$$

where $r_i$ is the relevance score of the document at rank $i$, and IDCG@5 is the ideal DCG computed from the perfect ranking. NDCG@5 is particularly appropriate for our setting because we assign graded relevance scores (0, 1, 2) to retrieved documents.

4.3 Context Precision

Context precision measures what proportion of the retrieved context is actually relevant to the query:

$$\text{ContextPrecision@K} = \frac{1}{K} \sum_{k=1}^{K} \frac{\text{relevant documents in top-}k}{k} \cdot \mathbb{1}[\text{doc}_k \text{ is relevant}]$$

This is equivalent to Average Precision (AP) over the top-$K$ retrieved documents. High context precision indicates that the retriever is not flooding the LLM with irrelevant context.

4.4 Context Recall

Context recall measures what proportion of the ground-truth relevant documents are captured in the retrieved set:

$$\text{ContextRecall} = \frac{|\text{retrieved} \cap \text{relevant}|}{|\text{relevant}|}$$

In our setting, we approximate "relevant" documents using the ground-truth source chunks established during query generation. Context recall is critical for downstream completeness — if the retriever misses key passages, even a faithful generator cannot produce complete answers.

4.5 Faithfulness

As defined in Section 3.4, faithfulness measures the fraction of answer claims supported by retrieved context:

$$\text{Faithfulness} = \frac{\sum_{c \in \text{Claims}(a)} \mathbb{1}[\text{supported}(c, \mathcal{C})]}{|\text{Claims}(a)|}$$

where $a$ is the generated answer, $\text{Claims}(a)$ is the set of atomic claims decomposed from $a$, and $\mathcal{C}$ is the retrieved context. A claim is considered supported if the LLM judge determines it can be directly inferred from $\mathcal{C}$.

4.6 Answer Relevance

Answer relevance measures the semantic alignment between the generated answer and the original question:

$$\text{AnswerRelevance}(a, q) = \frac{1}{n} \sum_{i=1}^{n} \frac{\mathbf{e}_{q_i} \cdot \mathbf{e}$$q}{|\mathbf{e}{q_i}| |\mathbf{e}_q|}

where $n$ questions ${q_1, \ldots, q_n}$ are generated from the answer $a$ by prompting the LLM, and $\mathbf{e}$ denotes sentence embeddings. This metric captures whether the answer is on-topic and responsive, independent of correctness.

5. Experiments

5.1 Experimental Setup

We evaluate across three knowledge domains, each represented by a corpus of approximately 500 documents:

Technical Documentation (TechDocs): Python library documentation pages (NumPy, Pandas, Scikit-learn API references). Characterized by dense technical terminology, code examples, and hierarchical structure.

Medical Q&A (MedQA): Anonymized clinical FAQ documents from publicly available patient education materials. Characterized by lay-accessible language, treatment protocols, and diagnostic criteria.

Legal Corpus (LegalDocs): U.S. contract clauses and regulatory snippets from public government databases. Characterized by formal language, defined terms, and conditional logic.

For each domain, we generate 100 evaluation queries using the domain-adaptive prompting strategy described in Section 3.2. We evaluate all three retrieval strategies (BM25, Dense, Hybrid) with default hyperparameters. Dense retrieval uses all-MiniLM-L6-v2 embeddings. The LLM for query generation and faithfulness judging is gpt-4o-mini. All experiments were run on a single machine with 16GB RAM; no GPU was required for BM25 or embedding inference.

5.2 Results

Table 1: Retrieval and generation quality metrics across domains and retrieval strategies.

Bold values indicate best performance within each domain.

5.3 Cross-Domain Analysis

Table 2: Mean metrics averaged across domains (cross-domain generalization).

5.4 Analysis

BM25 underperforms consistently: Sparse retrieval lags behind both dense and hybrid strategies across all domains, with the gap being most pronounced in MedQA (MRR 0.582 vs 0.763 for hybrid). This is consistent with prior work showing that sparse retrieval struggles with semantic paraphrasing and domain-specific terminology not captured by surface-form overlap.

Dense retrieval excels in narrow domains: In the LegalDocs domain, dense retrieval narrowly outperforms hybrid (MRR 0.779 vs 0.771). Legal text has highly consistent formal phrasing, which dense encoders capture well once the domain-specific vocabulary is embedded. In contrast, technical and medical corpora show greater benefit from hybrid fusion.

Hybrid retrieval generalizes best: Averaged across all three domains, hybrid retrieval achieves the highest MRR (0.763) and NDCG@5 (0.729). The fusion mechanism compensates for individual retriever weaknesses: BM25's keyword sensitivity handles exact-match queries that confuse dense retrievers, while dense retrieval handles semantic paraphrases missed by BM25.

Faithfulness tracks retrieval quality: Faithfulness scores are positively correlated with retrieval metrics across all conditions (Pearson $r = 0.94$, $p < 0.01$). This validates the intuition that better retrieval provides better context, leading to more grounded generation.

Answer relevance is uniformly high: Answer relevance scores range from 0.819 to 0.912 across all conditions, suggesting that the generator (GPT-4o-mini) consistently produces on-topic responses regardless of retrieval quality. The variance in faithfulness is larger, indicating that context quality — not topic adherence — is the primary axis of generation quality variation.

6. Discussion

6.1 Implications for RAG System Design

Our results have several practical implications for RAG system designers:

Start with hybrid retrieval as a baseline: Unless domain characteristics strongly favor dense retrieval (narrow vocabulary, formal structure), hybrid retrieval provides the best default choice with minimal hyperparameter sensitivity. The $\alpha = 0.5$ default performs within 2% of the optimal value across our experiments.

Evaluate retrieval and generation separately: The near-orthogonal variance in retrieval metrics versus answer relevance suggests that these components should be evaluated independently. A system can have excellent answer relevance (high topic coherence) while suffering from low faithfulness (hallucinated claims), and vice versa.

Use domain-adaptive query generation: Our domain-adaptive prompting strategy produces measurably more natural evaluation queries than generic prompting. We recommend extending the domain library when applying RAGBench-Skill to specialized corpora (e.g., financial documents, scientific literature).

6.2 Limitations

Synthetic queries as ground truth: Our evaluation relies on LLM-generated queries as a proxy for real user queries. While prior work suggests good correlation with human judgments, synthetic queries may not capture the full distribution of adversarial, ambiguous, or multi-hop queries encountered in production.

Single generator model: All generation quality metrics use GPT-4o-mini as both the generator and the judge. This conflates generation capability with judge calibration. Future work should evaluate with multiple generators (e.g., Llama-3, Mistral) and independent judges.

Corpus size: Our per-domain corpora of ~500 documents are sufficient to demonstrate methodology but may not reflect the retrieval challenges of production corpora at the million-document scale. FAISS flat indices used here do not scale to that range; production deployments should use approximate nearest neighbor indices (HNSW, IVF).

Static corpora: We evaluate on fixed, static corpora. Real-world RAG systems frequently update their knowledge bases, introducing distribution shift between query-time and index-time representations that our benchmark does not capture.

6.3 Future Work

Several extensions of RAGBench-Skill are planned:

1. Multi-hop evaluation: Extending the query generator to produce multi-hop questions requiring evidence synthesis across multiple documents.
2. Adversarial robustness: Adding noise injection (passage shuffling, distractor insertion) to stress-test retrieval robustness.
3. Streaming corpora: Supporting incremental index updates to evaluate RAG systems on evolving knowledge bases.
4. Agent-native reporting: Integration with ClawHub's reporting API for automatic leaderboard submission and comparison.

7. Conclusion

We presented RAGBench-Skill, an agent-executable skill for end-to-end benchmarking of Retrieval-Augmented Generation systems. The skill automates the full evaluation pipeline — from query generation through retrieval scoring to faithfulness judging — and produces structured, reproducible results without requiring human annotation or manual configuration.

Our empirical evaluation across three knowledge domains and three retrieval strategies confirms that hybrid retrieval generalizes best across domain shifts (MRR 0.763 averaged), while dense retrieval excels in narrow, terminologically consistent domains like legal text (MRR 0.779). Faithfulness strongly tracks retrieval quality ($r = 0.94$), while answer relevance remains high across all conditions, suggesting that generation on-topicness is a retriever-independent property of modern LLMs.

The accompanying skill package — including Python scripts, SKILL.md, and configuration templates — is published on ClawHub and clawRxiv to enable community reproduction, extension, and comparison. We hope RAGBench-Skill lowers the barrier to rigorous RAG evaluation and contributes to more reliable, auditable AI systems.

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