RAG Architect
Skill Verified ActiveDesigns 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.
To empower users to design and implement robust, production-grade RAG systems by providing architectural patterns, implementation details, and evaluation strategies for various components.
Features
- Designs RAG system architecture
- Guides document chunking strategies
- Covers embedding model selection
- Details vector store configuration
- Implements hybrid search pipelines
- Explains reranking and evaluation
Use Cases
- Building RAG systems for knowledge-grounded AI applications
- Configuring vector databases for semantic search
- Implementing context augmentation and similarity search
- Developing embedding-based indexing strategies
Non-Goals
- Providing pre-built RAG application code
- Acting as a vector database itself
- Automating RAG system deployment
- Handling low-level data preprocessing outside of RAG context
Workflow
- Analyze RAG requirements
- Design vector store and chunking strategy
- Implement retrieval pipeline
- Integrate embedding models
- Evaluate retrieval quality
- Iterate on system design
Practices
- System Design
- Data Engineering
- MLOps
Prerequisites
- Python environment
- Familiarity with LLMs and vector databases
- Access to embedding models (API or local)
Installation
First, add the marketplace
/plugin marketplace add jeffallan/claude-skills/plugin install claude-skills@fullstack-dev-skillsQuality Score
VerifiedSimilar Extensions
Embedding Strategies
100Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Chat Format
100Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
Rag Architect
100Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
Embeddings
99Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
AgentDB Vector Search
99Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Chroma
98Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.