# RAG Design

> Advanced design of Retrieval-Augmented Generation, focusing on multi-hop retrieval and query rewriting.

- Skill: `j4flmao/rag-design` (Agent Skill)
- Install (CLI): `npx skillmds@latest add j4flmao/rag-design`
- Raw SKILL.md: https://api.skillmd.com/api/skills/j4flmao/rag-design/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: j4flmao (https://skillmd.com/u/j4flmao)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/j4flmao/rag-design

---

# RAG Design Mechanics

## Multi-Hop Retrieval architecture
Complex queries often require assembling facts scattered across multiple documents. Multi-hop retrieval breaks down a compositional query into sub-queries.
- **Iterative Retrieval:** The system retrieves an initial document, extracts an entity/fact, and formulates a new search vector to traverse a knowledge graph or vector space until the termination condition is met.
- **Graph-based RAG:** Leverages knowledge graphs (e.g., Neo4j) where LLMs generate Cypher queries to traverse edges, providing exact relational context before falling back to dense vector similarity search (HNSW, IVF-PQ).

## Query Rewriting via LLM
Raw user queries are often heavily underspecified, containing lexical ambiguities or missing context.
- **Query Expansion/HyDE (Hypothetical Document Embeddings):** An LLM generates a hypothetical, hallucinated answer to the query. The embedding of this pseudo-document is then used to search the vector database, bridging the semantic gap between questions and answers.
- **Query Decomposition:** The LLM rewrites the single input into $N$ distinct queries targeting different facets of the problem.
- **Routing:** A small classifier or LLM router directs the rewritten queries to specific indices (e.g., tabular SQL DB, dense vector index, BM25 keyword index).

```mermaid
%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
flowchart TD
    User["Raw User Query"] -->|"Send()"| Rewriter["LLM Query Rewriter"]
    
    subgraph PreProcessingQueryTransformations ["Query Transformations<br><br><br>"]
        Rewriter -->|"HyDE / Expand"| Vectors["Search Vectors"]
        Rewriter -->|"Decompose"| SubQueries["Sub-Queries"]
    end
    
    subgraph RetrievalMultiHopEngine ["Multi-Hop Engine<br><br><br>"]
        Vectors --> Router["Index Router"]
        SubQueries --> Router
        Router -->|"Search(Dense)"| VectorDB["Vector DB"]
        Router -->|"Search(Graph)"| GraphDB["Knowledge Graph"]
        
        VectorDB -->|"Extract()"| Hop1["Intermediate Context"]
        Hop1 -->|"Rewrite()"| Router
    end
    
    GraphDB --> ContextWindow["Final Context Assembly"]
    VectorDB --> ContextWindow
    ContextWindow -->|"Generate()"| LLM["LLM Generator"]
```