# RAG Pipeline

> Deep guidance for RAG pipelines. Activates for chunking, embeddings, retrieval, vector stores, RAG optimization.

- Skill: `llama-farm/rag-pipeline` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add llama-farm/rag-pipeline`
- Raw SKILL.md: https://api.skillmd.com/api/skills/llama-farm/rag-pipeline/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: llama-farm (https://skillmd.com/u/llama-farm)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/llama-farm/rag-pipeline

---


# LlamaFarm RAG Pipeline Skill

Deep guidance for configuring RAG (Retrieval-Augmented Generation) pipelines.

## When to Load

Activate this skill when:
- User asks about RAG configuration
- User needs help with chunking, embeddings, or retrieval
- User is troubleshooting RAG quality issues
- User wants to optimize RAG performance

## RAG Pipeline Overview

```
Files → Parser → Chunks → Embedder → Vector Store → Retriever → LLM
         ↓
      Extractors → Metadata
```

## Quick Reference

### Chunk Size Guidelines

| Document Type | Chunk Size | Overlap |
|--------------|------------|---------|
| Legal/Regulatory | 1000-1200 | 150 |
| Technical Docs | 600-800 | 80-100 |
| Code | 500-600 | 50 |
| Notes/Markdown | 400-500 | 40-50 |
| Large PDFs | 900-1100 | 140-160 |

### Top-K Guidelines

| Use Case | Top-K |
|----------|-------|
| Quick Q&A | 3-5 |
| Research | 8-10 |
| Legal/Compliance | 10-15 |
| Code Search | 5-8 |

## Progressive Disclosure

Load these files for detailed guidance:

- **chunking.md** - Chunking strategies and configuration
- **embeddings.md** - Embedding model selection and setup
- **retrieval.md** - Retrieval strategy comparison
- **troubleshooting.md** - Diagnosing and fixing RAG issues