Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
v2.1.1: optimization, reference splits
What This Skill Does
RAG Architect covers the full lifecycle of retrieval-augmented generation pipelines, from document chunking and embedding selection to vector database setup, retrieval strategy tuning, and production deployment. It provides concrete guidance on tradeoffs between chunking methods, embedding models, and retrieval approaches including hybrid search and reranking.
Covers the entire RAG stack in one place, including tradeoffs that are typically scattered across separate documentation for chunking libraries, vector databases, and evaluation frameworks.
When to use it
- Building a Q&A chatbot over internal company documentation
- Choosing a vector database for a production deployment
- Debugging low retrieval precision in an existing RAG system
- Setting up RAGAS evaluation for a knowledge base search feature
- Reducing embedding API costs for a large document corpus
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: How should I structure a RAG pipeline for a 10,000-page technical manual?
- 1Analyzes document characteristics: structured technical content with headings and tables
- 2Recommends semantic or paragraph-based chunking with 10-20% overlap to preserve document structure
- 3Suggests a balanced embedding model such as all-mpnet-base-v2 for general technical content
- 4Recommends pgvector or Qdrant based on existing infrastructure and scale requirements
- 5Outlines RAGAS evaluation setup with faithfulness above 90% and context relevance above 0.8 as targets
A complete pipeline recommendation with specific chunking strategy, embedding model, vector database, and evaluation metrics tailored to the document type and scale