# evaluate-rag

Guides separate evaluation of RAG retrieval and generation, metric selection, and document chunking tests.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 970 installs · ★ 1557
- Author: ai-evals-course
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/ai-evals-course/evals-skills/tree/HEAD/skills/evaluate-rag/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/evaluate-rag

## What it does

The skill starts with end-to-end RAG traces: compare retrieved chunks with what the model needed, then separate retrieval failures from generation failures. Picking metrics before this analysis is premature.

For retrieval testing, it covers query-to-relevant-chunk datasets built manually or through synthetic questions based on document facts. Use Recall@k for first-pass retrieval; Precision@k, MRR, and NDCG@k fit reranking. It also describes adversarial questions with plausible distractor chunks.

Once retrieval has been checked, evaluate whether an answer is faithful to its context and relevant to the query. For chunking, the skill suggests a grid search over chunk size and overlap, re-indexing the corpus for each setting. Section boundaries and headings can help when fixed-size chunks split related content.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add ai-evals-course/evals-skills --skill evaluate-rag -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/evaluate-rag/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/evaluate-rag/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add ai-evals-course/evals-skills --skill evaluate-rag -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/evaluate-rag/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add ai-evals-course/evals-skills --skill evaluate-rag -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/evaluate-rag/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install ai-evals-course/evals-skills evaluate-rag
```

2. **Manually: put the files in place**
- File: `.github/skills/evaluate-rag/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add ai-evals-course/evals-skills --skill evaluate-rag -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/ai-evals-course/evals-skills --path skills/evaluate-rag
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/evaluate-rag/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add ai-evals-course/evals-skills --skill evaluate-rag -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/ai-evals-course/evals-skills/tree/HEAD/skills/evaluate-rag/SKILL.md

## When to use it
- separate retrieval failures from generation failures
- build a query and relevant-chunk evaluation set
- measure first-pass retrieval with Recall@k
- check answer faithfulness and relevance
- tune chunk size and overlap

## Example prompts
- Evaluate my RAG traces and identify whether the failure is in retrieval or generation.
- Create a retrieval evaluation set with questions and ground-truth chunks from my documents.
- Test chunking settings with a grid search using Recall@k and NDCG@k.

## Source text · SKILL.md · Apache-2.0

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````text
---
name: evaluate-rag
description: >
  Guides evaluation of RAG pipeline retrieval and generation quality.
  Use when evaluating a retrieval-augmented generation system, measuring retrieval quality,
  assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval
  testing, or optimizing chunking strategies.
---

# Evaluate RAG

## Overview

1. Do error analysis on end-to-end traces first. Determine whether failures come from retrieval, generation, or both.
2. Build a retrieval evaluation dataset: queries paired with relevant document chunks.
3. Measure retrieval quality with Recall@k (most important for first-pass retrieval).
4. Evaluate generation separately: faithfulness (grounded in context?) and relevance (answers the query?).
5. If retrieval is the bottleneck, optimize chunking via grid search before tuning generation.

## Prerequisites

Complete error analysis on RAG pipeline traces before selecting metrics. Inspect what was retrieved vs. what the model needed. Determine whether the problem is retrieval, generation, or both. Fix retrieval first.

## Core Instructions

### Evaluate Retrieval and Generation Separately

Measure each component independently. Use the appropriate metric for each retrieval stage:

- **First-pass retrieval:** Optimize for Recall@k. Include all relevant documents, even at the cost of noise.
- **Reranking:** Optimize for Precision@k, MRR, or NDCG@k. Rank the most relevant documents first.

### Building a Retrieval Evaluation Dataset

You need queries paired with ground-truth relevant document chunks.

**Manual curation (highest quality):** Write realistic questions and map each to the exact chunk(s) containing the answer.

**Synthetic QA generation (scalable):** For each document chunk, prompt an LLM to extract a fact and generate a question answerable only from that fact.

Synthetic QA prompt template:

```
Given a chunk of text, extract a specific, self-contained fact from it.
Then write a question that is directly and unambiguously answered
by that fact alone.

Return output in JSON format:
{ "fact": "...", "question": "..." }

Chunk: "{text_chunk}"
```

**Adversarial question generation:** Create harder queries that resemble content in multiple chunks but are only answered by one.

Process:
1. Select target chunk A containing a clear fact.
2. Find similar chunks B, C using embedding search (chunks that share terminology but lack the answer).
3. Prompt the LLM to write a question using terminology from B and C that only chunk A answers.

Example:
- Chunk A: "In April 2020, the company reported a 17% drop in quarterly revenue, its largest decline since 2008."
- Chunk B: "The company experienced significant losses in 2008 during the financial crisis."
- Generated question: "When did the company experience its largest revenue decline since the 2008 financial crisis?"
````
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