# rag

Guides the creation of Retrieval-Augmented Generation pipelines, covering document chunking, embedding generation, vector database selection, and retrieval.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 3700 installs · ★ 363
- Author: giuseppe-trisciuoglio
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/giuseppe-trisciuoglio/developer-kit/tree/HEAD/plugins/developer-kit-ai/skills/rag/SKILL.md
- Updated: 2026-09-10
- HTML: https://affpapa.org/en/nevibecode/skills/rag

## What it does

This skill guides the implementation of RAG pipelines with step-by-step engineering instructions. It covers document preprocessing, chunking into 500–1000 token segments with 10–20% overlap, and generating embeddings with models like bge-large-en-v1.5 or text-embedding-ada-002. The guide maps out storage choices directly: Pinecone and Milvus for production scale, Weaviate and Qdrant for open-source stacks, and Chroma or FAISS for local setups.

Retrieval patterns include dense semantic search, metadata filtering, hybrid search with BM25, and cross-encoder reranking. The pipeline includes retry logic capped at 3 attempts for batch embedding jobs encountering `EmbeddingException`. Security guidance requires sanitizing external documents before feeding them to the language model to stop prompt injection attacks.

Poor chunk boundaries break semantic context. The skill enforces vector dimension validation right after generation so corrupt or empty vectors never reach the database.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill rag -a claude-code
```

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

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill rag -a codex
```

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

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill rag -a cursor
```

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

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install giuseppe-trisciuoglio/developer-kit rag
```

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

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill rag -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/giuseppe-trisciuoglio/developer-kit --path plugins/developer-kit-ai/skills/rag
```

### Windsurf (Devin Desktop)

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

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill 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/giuseppe-trisciuoglio/developer-kit/tree/HEAD/plugins/developer-kit-ai/skills/rag/SKILL.md

## When to use it
- build document Q&A systems over proprietary documentation
- select a vector database suited for scale or local prototyping
- split documents into chunks with boundary overlap
- add cross-encoder reranking to retrieval results
- handle batch embedding ingestion with automatic retries

## Example prompts
- Set up a RAG pipeline for documentation using Qdrant and the bge-large-en-v1.5 embedding model
- Configure metadata-filtered retrieval with category=technical and minimum score 0.7
- Implement a document assistant with MessageWindowChatMemory storing 10 messages

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: b269dc19129b1104.

BEGIN THIRD-PARTY FILE b269dc19129b1104
````text
---
name: rag
description: Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
allowed-tools: Read, Write, Bash
---

# RAG Implementation

Build Retrieval-Augmented Generation systems that extend AI capabilities with external knowledge sources.

## Overview

This skill covers: document processing, embedding generation, vector storage, retrieval configuration, and RAG pipeline implementation.

## When to Use

- Building Q&A systems over proprietary documents
- Creating chatbots with factual information from knowledge bases
- Implementing semantic search with natural language queries
- Reducing hallucinations with grounded, sourced responses
- Building documentation assistants and research tools
- Enabling AI systems to access domain-specific knowledge

## Instructions

### Step 1: Choose Vector Database

Select based on your requirements:

| Requirement | Recommended |
|-------------|-------------|
| Production scalability | Pinecone, Milvus |
| Open-source | Weaviate, Qdrant |
| Local development | Chroma, FAISS |
| Hybrid search | Weaviate with BM25 |

### Step 2: Select Embedding Model

| Use Case | Model |
|----------|-------|
| General purpose | text-embedding-ada-002 |
| Fast and lightweight | all-MiniLM-L6-v2 |
| Multilingual | e5-large-v2 |
| Best performance | bge-large-en-v1.5 |

### Step 3: Implement Document Processing Pipeline

1. Load documents from source (file system, database, API)
2. Clean and preprocess (remove formatting, normalize text)
3. Split documents into chunks with appropriate strategy
4. Generate embeddings for each chunk
5. Store embeddings in vector database with metadata

**Validation**: Verify embeddings were generated successfully:
```java
List<Embedding> embeddings = embeddingModel.embedAll(segments);
if (embeddings.isEmpty() || embeddings.get(0).dimension() != expectedDim) {
    throw new IllegalStateException("Embedding generation failed");
}
```

### Step 4: Configure Retrieval Strategy

Choose the appropriate strategy:

- **Dense Retrieval**: Semantic similarity via embeddings (default for most cases)
- **Hybrid Search**: Dense + sparse retrieval for better coverage
- **Metadata Filtering**: Filter by document attributes
- **Reranking**: Cross-encoder reranking for high-precision requirements

### Step 5: Build RAG Pipeline

1. Create content retriever with your embedding store
2. Configure AI service with retriever and chat memory
````
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