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RAG Architecture Designer

Designs production-grade Retrieval-Augmented Generation pipelines including chunking strategies, embedding model selection, vector database architecture, hybrid search, reranking, and hallucination reduction techniques.

Gold
v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

84
Research Quality (15%)
8.5
Prompt Engineering (25%)
8.5
Practical Utility (15%)
8.5
Completeness (10%)
8.5
User Satisfaction (20%)
8
Decision Usefulness (15%)
8.5

Best for

  • Design enterprise RAG systems for customer support with 99.5% uptime requirements and <500ms latency
  • Architect hybrid search pipelines combining vector similarity with BM25 for legal document retrieval
  • Optimize RAG chunking strategies for technical documentation with complex nested structures
  • Build production RAG systems handling 10M+ documents with real-time updates and version control
  • Design multi-modal RAG architectures integrating text, code, and structured data with consistent embedding spaces

What you'll get

  • Detailed architecture diagram with specific vector DB configuration (HNSW index parameters, sharding strategy), embedding model selection rationale, and chunking strategy with overlap percentages
  • Production deployment checklist with monitoring metrics, fallback strategies, and performance optimization recommendations for sub-500ms query response times
  • Technical implementation guide with code patterns for hybrid search, reranking algorithms, and hallucination detection mechanisms
Not designed for ↓
  • ×Basic chatbot development without retrieval components
  • ×Simple vector database setup tutorials or basic embedding model comparisons
  • ×Training custom LLMs from scratch or fine-tuning foundation models
  • ×Frontend user interface design for RAG applications
Expects

Detailed requirements including data types, query patterns, scale expectations, latency constraints, and accuracy thresholds for the RAG system.

Returns

Complete RAG architecture blueprint with specific technology recommendations, configuration parameters, chunking strategies, and production deployment considerations.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

ragretrieval-augmented-generationvector-databaseembeddingschunkinghybrid-searchrerankingpineconepgvectorweaviatellminformation-retrievalhallucination-reduction

Research Foundation: 8 sources (1 academic, 5 official docs, 2 paper)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/14/2026

Initial release

Works well with

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Common Workflows

Enterprise RAG Implementation

Complete RAG system design, database optimization, monitoring setup, and performance validation for enterprise deployment

rag-architecture-designerVector Database OptimizationLLM Observability Engineerapi-performance-testing-expert

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