Rag architect
Design RAG pipelines with informed chunking, embedding, retrieval, and evaluation decisions. TRIGGER when: user asks about RAG pipeline design, chunking strategies, embedding models, vector databases, or retrieval-augmented generation. DO NOT TRIGGER when: user asks about fine-tuning, prompt engineering without retrieval, or general LLM usage.From its SKILL.md
npx -y skills add DROOdotFOO/agent-skills --skill rag-architectAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
2.8 KB, 467 tokens by cl100k_base, as published. Nobody here has run it
RAG Architect
Design retrieval-augmented generation pipelines with the right tradeoffs at each layer.
Workflow
- Choose chunking strategy -- Match chunk method to document structure
- Select embedding model -- Balance dimensions, speed, and domain fit
- Choose vector DB -- Match scale, features, and deployment model
- Design retrieval -- Dense, sparse, hybrid, or reranked
- Evaluate -- Measure faithfulness, relevance, and answer quality
Reading Guide
| Decision | File |
|---|---|
| Chunking strategies + embedding models | chunking-and-embedding.md |
| Retrieval strategies + vector DBs + evaluation | retrieval-and-evaluation.md |
Quick Decision Matrix
| Document type | Chunking | Embedding | Retrieval |
|---|---|---|---|
| Code | Semantic (AST-aware) | Code-specialized | Hybrid + rerank |
| Legal/medical | Document-aware (sections) | Domain-specific | Dense + rerank |
| Chat logs | Sentence | General-purpose | Dense |
| Technical docs | Recursive | General-purpose | Hybrid |
| Mixed/unknown | Recursive (fallback) | General-purpose | Hybrid + rerank |
What You Get
- A RAG pipeline architecture specifying chunking strategy, embedding model, vector store, and retrieval method for your document types
- Concrete configuration recommendations (chunk size, overlap, dimensions, top-k) with rationale for each tradeoff
- An evaluation plan using RAGAS or equivalent metrics to validate retrieval quality before and after tuning
Rules
- Start simple -- fixed-size chunks + dense retrieval is a valid baseline
- Measure before optimizing -- run RAGAS evaluation before adding complexity
- Chunk overlap matters -- 10-20% overlap prevents context loss at boundaries
- Embedding dimensions are a tradeoff -- higher is not always better (cost, latency)
- Hybrid retrieval (dense + sparse) almost always beats either alone
What ships with it: 2 files
10.5 KB alongside SKILL.md