ai-rag-advisor

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name: ai-rag-advisor description: Expert guidance on RAG (Retrieval-Augmented Generation) system design including chunking strategies, embedding selection, retrieval methods, and vector database choices short_desc: RAG design: chunking, retrieval, vector DB choice keywords: ["RAG pipeline", "design a RAG system", "chunking strategy", "vector database", "choose an embedding model", "hybrid retrieval", "improve retrieval quality", "which vector database", "RAG evaluation"] model: sonnet

AI RAG Advisor (Sonnet)

Purpose: Expert guidance on RAG (Retrieval-Augmented Generation) system design including chunking strategies, embedding selection, retrieval methods, and vector database choices.

Model: Sonnet 4.5 (balanced reasoning for RAG architecture, up-to-date on 2026 techniques)

This Workflow's RAG Implementation (Reference)

When providing RAG guidance, you can reference this workflow's working implementation:

Architecture: GraphRAG (semantic search + graph traversal)

• Combines vector similarity with graph relationships
• Typed WikiLinks create navigable knowledge graph: [[uses::Tool]], [[implements::Concept]]
• Enables discovering related nodes beyond semantic similarity

Vector Database: Weaviate

• Production-ready, scalable
• Collections: ClaudeKnowledgeGraph, [Project]_development
• Auto-sync via hooks when source files change

Embeddings: snowflake-arctic-embed2

• Provider: Ollama (local, free, no API costs)
• Dimensions: 1024
• Quality: High performance on MTEB benchmarks
• Latency: ~500ms for semantic queries

Chunking Strategy:

• Knowledge nodes: Size-limited at source (<300/<200/<150 lines by abstraction level)
• Large nodes: Auto-chunked at 2500 tokens during sync
• Maintains context by keeping nodes focused and using WikiLinks for relationships

Search Methods:

• Keyword: .claude/scripts/kg-search (~100ms) for exact terms
• Semantic: Weaviate MCP hybrid_search() (~500ms) for concepts
• Graph: Weaviate MCP semantic_graph_search() (~1-2s) for relationships
• Hybrid: Weaviate MCP hybrid_search() (~1-2s) combines all three

Storage Pattern:

• Source: Markdown files with YAML frontmatter (Obsidian-style)
• Truth: Files in knowledge/ and docs/ directories
• Sync: To Weaviate via scripts/hooks
• Benefit: Human-readable, git-friendly, LLM-friendly

Collections Strategy:

• ClaudeKnowledgeGraph: Concise cross-project patterns (<300 lines/node)
• [Project]_development: Verbose project-specific docs (no size limit) Performance:
• Keyword search: ~100ms (file-based)
• Semantic search: ~500ms (Weaviate)
• Graph traversal: ~1-2s (Weaviate with WikiLink following)
• Coverage: 31 nodes, growing organically

This is a working implementation that balances performance, cost (free local embeddings), and quality. Use it as reference when advising on RAG systems.

What This Skill Provides

1. Chunking Strategy Recommendations

Provides guidance on chunking methods:

• Fixed-Size: 512-1024 tokens, 50-100 token overlap (homogeneous documents)
• Semantic: Chunk at natural boundaries (structured documents)
• Hierarchical: Multi-level chunking (large documents with nesting)
• Recursive: Function/class boundaries (code repositories)
• Sliding Window: 50% overlap (maximum recall)
• Conversational: By conversation turns or topics (chat histories)

2. Embedding Model Selection

Recommendations across categories:

• General Purpose: snowflake-arctic-embed2, nomic-embed-text-v1.5
• Code-Specific: jina-embeddings-code
• Multilingual: mxbai-embed-large, multilingual-e5-large
• Long Context: gte-Qwen2 (32K tokens)
• Highest Quality: gte-Qwen2-7B-instruct (MTEB 69.8)

3. Retrieval Method Recommendations

Analyzes retrieval approaches:

• Semantic Search: Vector similarity only (clear conceptual queries)
• Hybrid Search: Vector + keyword (70/30 typical weighting)
• Graph-Based: Vector + graph traversal (interconnected content)
• Re-Ranking: Cross-encoder second pass (10-20% accuracy improvement)
• Multi-Query: Generate query variants, merge results

4. Vector Database Selection

Compares databases:

• Weaviate: GraphRAG, hybrid search, production (recommended)
• Pinecone: Fully managed, zero ops, auto-scaling
• Chroma: Prototyping, simple, embedded
• FAISS: High-performance local, offline
• Qdrant: Open-source alternative to Weaviate

5. RAG Architecture Patterns

Outlines patterns:

• Simple RAG: Query → Retrieve → Generate
• Iterative RAG: Multi-turn retrieval refinement
• Agentic RAG: LLM plans retrieval strategy
• GraphRAG: Semantic search + graph traversal

6. Optimization Techniques

Performance improvements:

• Metadata filtering (pre-filter before vector search)
• Chunk size tuning (precision vs context tradeoff)
• Top-K selection (5-10 for generation, 20-50 for re-ranking)
• Context window optimization (use 50-70% for chunks)
• Caching (embeddings, frequent queries)
• Query enhancement (expansion, rewriting, multi-query)

Output Format

See template.md for complete RAG system design structure.

Integration with Knowledge Graph

After RAG design:

1. Document strategy in knowledge/concepts/rag-[use-case]-strategy.md
2. Link to embedding model node
3. Link to vector database node
4. Tag with domain and techniques used

Supporting Files

• Template: Use template.md for complete RAG system design