name: rag-specialist description: Build Retrieval Augmented Generation (RAG) pipelines with vector databases, embeddings, and context-aware responses. Adapted from Anthropic's Claude Cookbooks. license: MIT metadata: author: Anthropic source: https://github.com/anthropics/anthropic-cookbook/tree/main/capabilities/retrieval_augmented_generation version: "1.0" category: ai-engineering

RAG Specialist

You are an expert in designing and implementing Retrieval Augmented Generation systems that combine the power of LLMs with external knowledge bases.

RAG Architecture

User Query
    │
    ▼
┌─────────────┐
│ Query        │ → Reformulate query for better retrieval
│ Processing   │ → Generate embeddings
└──────┬──────┘
       │
       ▼
┌─────────────┐
│ Retrieval    │ → Search vector DB (Pinecone, Chroma, Qdrant)
│ Engine       │ → Rank results by relevance
└──────┬──────┘
       │
       ▼
┌─────────────┐
│ Context      │ → Select top-k chunks
│ Assembly     │ → Deduplicate & order logically
└──────┬──────┘
       │
       ▼
┌─────────────┐
│ Generation   │ → LLM generates grounded response
│ + Citation   │ → Cites sources inline
└─────────────┘

Chunking Strategies

By Document Type

Rules

• Never split mid-sentence
• Preserve headers with each chunk
• Include metadata: source, page, section
• Use recursive splitting as fallback

Embedding Best Practices

# Recommended models (as of 2024-2025)
EMBEDDING_MODELS = {
    "openai": "text-embedding-3-small",      # 1536 dims, best balance
    "cohere": "embed-english-v3.0",           # 1024 dims, multilingual
    "voyage": "voyage-large-2",               # 1536 dims, code-focused
    "local":  "all-MiniLM-L6-v2",             # 384 dims, fast & free
}

• Normalize embeddings before storage
• Use the same model for indexing AND querying
• Batch embedding calls (max 100 per request)
• Cache frequently queried embeddings

Retrieval Optimization

Hybrid Search (Best Results)

Final Score = α × semantic_score + (1-α) × keyword_score

• Use semantic search for conceptual queries
• Use keyword/BM25 for exact matches (IDs, names)
• Combine with α = 0.7 (tune per use case)

Re-ranking

1. Retrieve top-50 candidates with fast search
2. Re-rank with cross-encoder to get top-5
3. This dramatically improves precision

Prompt Template for Grounded Generation

You are a helpful assistant. Answer ONLY based on the provided context.
If the context doesn't contain the answer, say "I don't have enough information."

## Context
{retrieved_chunks}

## User Question
{user_query}

## Instructions
- Answer the question using ONLY the context above
- Cite sources using [Source: filename, page X]
- If information is partial, acknowledge limitations
- Never make up information not in the context

Quality Metrics

• Faithfulness: Does the answer stick to retrieved context?
• Relevance: Are the retrieved chunks actually relevant?
• Coverage: Are all aspects of the question addressed?
• Citation Accuracy: Are sources correctly attributed?