# Rag Architect

Designs and implements production-grade RAG systems by chunking documents, generating...

- Repository: https://github.com/Jeffallan/claude-skills (12164 stars)
- Category: Design
- License: MIT
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Jeffallan/claude-skills --skill rag-architect -g -a claude-code -y`
Codex: `npx skills add Jeffallan/claude-skills --skill rag-architect -g -a codex -y`
Cursor: `npx skills add Jeffallan/claude-skills --skill rag-architect -g -a cursor -y`
Gemini CLI: `npx skills add Jeffallan/claude-skills --skill rag-architect -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Jeffallan/claude-skills`

## Description

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# RAG Architect

## Core Workflow

1. **Requirements Analysis**, Identify retrieval needs, latency constraints, accuracy requirements, and scale
2. **Vector Store Design**, Select database, schema design, indexing strategy, sharding approach
3. **Chunking Strategy**, Document splitting, overlap, semantic boundaries, metadata enrichment
4. **Retrieval Pipeline**, Embedding selection, query transformation, hybrid search, reranking
5. **Evaluation & Iteration**, Metrics tracking, retrieval debugging, continuous optimization

For each step, validate before moving on (see checkpoints below).

## Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When |
|-------|-----------|-----------|
| Vector Databases | `references/vector-databases.md` | Comparing Pinecone, Weaviate, Chroma, pgvector, Qdrant |
| Embedding Models | `references/embedding-models.md` | Selecting embeddings, fine-tuning, dimension trade-offs |
| Chunking Strategies | `references/chunking-strategies.md` | Document splitting, overlap, semantic chunking |
| Retrieval Optimization | `references/retrieval-optimization.md` | Hybrid search, reranking, query expansion, filtering |
| RAG Evaluation | `references/rag-evaluation.md` | Metrics, evaluation frameworks, debugging retrieval |

## Implementation Examples

### 1. Chunking Documents

```python
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Evaluate chunk_size on your domain data, never use 512 blindly
splitter = RecursiveCharacterTextSplitter(
    chunk_size=800,
    chunk_overlap=100,
    separators=["\n\n", "\n", ". ", " "],
)

chunks = splitter.create_documents(
    texts=[doc.page_content for doc in raw_docs],
    metadatas=[{"source": doc.metadata["source"], "timestamp": doc.metadata.get("timestamp")} for doc in raw_docs],
)
```

**Checkpoint:** `assert all(c.metadata.get("source") for c in chunks), "Missing source metadata"`

### 2. Generating Embeddings & Indexing

The rest: https://github.com/Jeffallan/claude-skills/blob/HEAD/skills/rag-architect/SKILL.md