Menu
Route each query to the right retrieval strategy, data source, or tool with an LLM-driven router.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Use this skill when building, debugging, or improving Retrieval-Augmented Generation systems, including chunking, vector database selection, hybrid search, reranking, multimodal RAG, code documentation RAG, retrieval latency, and production RAG architecture.
Chunk nested documents into parent-child levels so retrieval can move from broad sections to fine-grained passages.
Use semantic boundaries and embedding similarity to chunk text for higher-relevance retrieval.
Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.
Use overlapping windows to preserve context across chunk boundaries while controlling retrieval size.
Reduce retrieval latency with caching, batching, and index-level optimization.
| name | agentic-rag-routing |
| title | Agentic RAG Routing |
| description | Route each query to the right retrieval strategy, data source, or tool with an LLM-driven router. |
| allowed-tools | ["Read","Grep","Glob","Bash"] |
| category | rag-agents |
| tags | ["routing","tool-use","query-classification","agentic-rag"] |
Agentic routing puts a decision step in front of retrieval: the agent classifies the query and chooses whether to retrieve, which source or index to hit, and which tool to call. This turns a one-size-fits-all pipeline into one that adapts per query.
Different queries need different handling. A definitional question, a multi-hop question, and a live-data question all fail under a single fixed retrieval path. Without routing, the pipeline over-retrieves for simple queries and under-serves complex ones.
Enumerate the concrete routes available, such as no-retrieval, vector search, keyword search, web search, or a structured-data tool.
Why: A router can only choose from routes you have defined; an open-ended router is unpredictable and hard to evaluate.
Use an LLM or a lightweight classifier to map each query to a route, returning a structured decision rather than free text.
Why: Structured routing decisions are testable and can be logged, replayed, and evaluated.
Define a default route for low-confidence decisions and cap how many tools or hops a single query may trigger.
Why: Guardrails prevent runaway tool loops and keep latency and cost bounded.
For patterns, see the LangGraph agentic RAG guide and the LlamaIndex router query engine.
