Query Routing : Towards Advance RAG Systems

🧭Introduction

This article is part of the Advance RAG Series, an article series which explains the tenets and features of Advance RAG Systems with the help of visuals and code.

Query Routing refers to directing the query either logically or semantically towards the best possible destination. Since an optimised way to do operations is a hallmark of efficiency hence good and optimised query routing is crucial for building an efficient and advance RAG systems.

🚦Query Routing : Right Place, Right Destination, Right Journey

In order to achieve efficiency, optimal results and best output from any action the right direction is crucial.

Similarly, in advanced Retrieval-Augmented Generation (RAG) systems, mere query translation is not sufficient. In real-world applications, data is often fragmented, dispersed, diverse, and sparse. Therefore, during the indexing phase, it is critical to perform intelligent routing—ensuring that data is stored in the correct database or within the appropriate collection inside a single database.

Likewise, during the retrieval phase, fetching the most relevant data chunks for context feeding is equally important. This not only involves identifying the relevant content, but also determining where that content is stored and how it can be efficiently accessed.

Moreover, each model responds differently depending on its pretraining objectives—for example, one model might excel at coding tasks while another may perform better in creative or linguistic reasoning. In all these scenarios, the ability to choose the right path or component—whether it's a database, collection, model, or even routing strategy—can significantly impact the quality and relevance of the final response.

Thus, query routing emerges as a critical capability—enabling RAG systems to make intelligent decisions across indexing, retrieval, and generation stages for optimal performance.

🔀Types Of Routing

There are two types of Query Routing possible :

🧠Logical Routing Diagram and Code

// Logical routing function
function logicalRouter(userPrompt) {
  const collections = {
    financial: ["invoice", "payment", "budget", "revenue", "expense", "finance", "cost"],
    employee: ["employee", "hiring", "recruitment", "payroll", "salary", "leave", "attendance", "promotion"],
    feedback: ["feedback", "review", "complaint", "suggestion", "rating", "survey", "opinion"]
  };

  const promptLower = userPrompt.toLowerCase();

  for (const [collectionName, keywords] of Object.entries(collections)) {
    for (const keyword of keywords) {
      if (promptLower.includes(keyword)) {
        return collectionName;  
      }
    }
  }
  return "general"; 
}

// Example usage:
const userQuery = "Can you pull last quarter's revenue reports?";
const routedCollection = logicalRouter(userQuery);

console.log("Selected Collection:", routedCollection);

// Output: Selected Collection: financial
,

🧬Semantic Routing Prompt Example

"You are a Semantic Router. Your task is to understand the user's query based on its meaning 
and decide the best route for it.

Carefully analyze the intent, topic, and context of the query, and select the most appropriate destination from the available options.

Focus only on the semantic meaning of the query, not just keywords. 
Respond only with the chosen destination."

"You are a Semantic Router.
Your task is to deeply understand the meaning and intent behind the user's query.
Fill in the following variables based on your understanding:

    {action} → What is the user trying to do? (e.g., store, retrieve, summarize, generate, translate, analyze)

    {topic} → What is the query about? (e.g., documents, coding, travel, marketing, data)

    {target_model} → Which type of model should handle it? (e.g., general LLM, code LLM, retrieval system, summarization model)

    {urgency} → Is it urgent, normal, or can be queued?]

Analyze the query carefully, rely on semantic meaning rather than keywords, and fill these fields accurately. If information is missing, infer sensibly."

Conclusion

In modern Advanced RAG (Retrieval-Augmented Generation) systems, Query Routing is not merely a convenience—it's a foundational requirement. As real-world data continues to grow in complexity, fragmentation, and contextual depth, the ability to route queries intelligently becomes essential for achieving optimal system performance and reliability.

This article explored two key routing strategies:

• 🧠 Logical Routing operates on predefined rules and metadata, offering straightforward and predictable behavior. It is particularly effective in structured environments where data is clearly categorized.

• 🧬 Semantic Routing utilizes the semantic understanding of queries through large language models (LLMs), making it well-suited for dynamic, unstructured, or evolving scenarios where intent is not explicitly stated.

By integrating routing logic with ranking mechanisms and LLM-driven prompts, Advanced RAG systems are empowered to determine the most appropriate database, collection, or model for handling a given query. This ensures that each query follows the most efficient path to retrieve high-quality, contextually relevant information.

Ultimately, effective query routing enables RAG systems to go beyond basic retrieval, transforming them into intelligent orchestration engines capable of making context-aware decisions across the indexing, retrieval, and generation stages—delivering better responses, faster outcomes, and a more refined user experience.