rag-architect

name	rag-architect
description	Best practices for Retrieval-Augmented Generation. Designing, implementing, and optimizing production-grade RAG pipelines.

RAG Architect - POWERFUL

The RAG (Retrieval-Augmented Generation) Architect skill provides comprehensive tools and knowledge for designing, implementing, and optimizing production-grade RAG pipelines. This skill covers the entire RAG ecosystem from document chunking strategies to evaluation frameworks, enabling you to build scalable, efficient, and accurate retrieval systems.

Core Competencies

3. Vector Database Selection

Pinecone

• Managed service: Fully hosted, auto-scaling
• Best for: Production applications, when managed service is preferred

Weaviate

• Open source: Self-hosted or cloud options available
• Best for: Complex data types, when GraphQL API is preferred

pgvector (PostgreSQL)

• SQL integration: Leverage existing PostgreSQL infrastructure
• Best for: When you already use PostgreSQL, need ACID compliance

4. Retrieval Strategies

Hybrid Retrieval

• Combination approach: Dense + sparse retrieval with score fusion
• Benefits: Combines semantic understanding with exact matching

Reranking

• Two-stage approach: Initial retrieval followed by reranking
• Benefits: Higher precision, can use more sophisticated models for final ranking

5. Query Transformation Techniques

HyDE (Hypothetical Document Embeddings)

• Approach: Generate hypothetical answer, embed answer instead of query
• Benefits: Improves retrieval by matching document style

Multi-Query Generation

• Approach: Generate multiple query variations, retrieve for each, merge results
• Benefits: Increases recall, handles query ambiguity

Implementation Best Practices

Development Workflow

1. Requirements gathering
2. Data analysis
3. Prototype development
4. Chunking optimization
5. Retrieval tuning
6. Evaluation setup
7. Production deployment

Monitoring & Observability

• Query analytics: Track query patterns
• Retrieval metrics: Monitor precision, recall, and latency
• Generation quality: Track faithfulness and relevance
• Cost tracking: Monitor embedding and vector database costs