From systems-design
Designs end-to-end RAG architectures for use cases like customer support chatbots, documentation Q&A, legal search, and code assistance, covering ingestion pipelines, retrieval strategies, quality metrics, and scaling.
npx claudepluginhub melodic-software/claude-code-plugins --plugin systems-designThis skill is limited to using the following tools:
Design a Retrieval-Augmented Generation system for a given use case.
Covers RAG architecture including design patterns, chunking strategies, embedding models, retrieval techniques, hybrid search, and context assembly for LLM pipelines.
Designs production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, reranking, and evaluating retrieval. For RAG, vector DBs, semantic search apps.
Share bugs, ideas, or general feedback.
Design a Retrieval-Augmented Generation system for a given use case.
$ARGUMENTS - The RAG use case to design for (e.g., "customer support chatbot", "documentation Q&A", "legal document search", "code assistant")
Clarify requirements by understanding:
Load relevant skills based on the use case:
rag-architecturevector-databasesllm-serving-patternsml-inference-optimizationSpawn the rag-architect agent for comprehensive design:
Design the ingestion pipeline:
Design the retrieval pipeline:
Address quality and scale:
/sd:rag-design customer support chatbot with 10K FAQ documents
/sd:rag-design internal documentation Q&A for engineering team
/sd:rag-design legal document search for contract review
/sd:rag-design code assistant for enterprise codebase
/sd:rag-design research paper Q&A with 100K papers
/sd:rag-design product catalog search with structured data
/sd:rag-design multi-lingual knowledge base
| Category | Key Considerations |
|---|---|
| Customer Support | FAQ coverage, escalation, tone consistency |
| Documentation | Technical accuracy, code examples, versioning |
| Legal/Compliance | Citation accuracy, audit trails, access control |
| Code Assistance | AST-aware chunking, context relevance, IDE integration |
| Research/Academic | Multi-document reasoning, citation, long-form answers |
| E-commerce | Product attributes, inventory awareness, personalization |
| Complexity | Pattern | When to Use |
|---|---|---|
| Low | Basic RAG | Simple Q&A, small corpus |
| Medium | RAG + Reranking | Higher accuracy needed |
| Medium | Hybrid Search | Mixed keyword + semantic queries |
| High | Query-Transformed | Vague or complex queries |
| High | Agentic RAG | Multi-hop reasoning, tool use |
A comprehensive RAG system architecture including: