Optimizing RAG: Dynamic Query Routing for Multi-Source Answer Generation

Question

I'm looking for guidance on optimizing our current RAG (Retrieval Augmented Generation) workflow using Azure AI Search. Right now, our system processes a user query by generating search parameters, retrieving context from Azure AI Search, and then combining that with the query to generate a final answer. While this works for some queries, it doesn't fully accommodate the diverse nature of user questions.

For example:

• Internal Knowledge Only: A query like "What creative strategies can we explore for a new product launch?" might be answered solely by the model’s internal knowledge.

Company-Specific Data: A query such as "What are the current trends in our industry based on internal performance data?" should tap into a vector database containing company-specific insights.

External Web Search: A question like "What are the latest market trends in healthcare technology?" requires the most current external data from a web search.

Integrated Business Strategy: For a more complex case, consider a query like "Based on the XY business model, how can we create a comprehensive business strategy or marketing campaign?" This would require the model to understand the business model, consider company-specific data, and integrate external insights to deliver a tailored strategy.

My thought is to enhance the current RAG setup by first breaking down the query into subtasks—where the model generates an initial answer, then enriches that answer by retrieving additional context from the most appropriate data source (whether internal, company-specific, or external), and finally synthesizes a richer final output.

I’m considering two approaches:

Multiple Agents: Each specialized in a different part of the process.

Dynamic Routing in Code: Using an LLM to analyze and decompose the query, decide on the optimal data source, and orchestrate the retrieval and synthesis steps.

Has anyone implemented a similar multi-faceted workflow? What strategies or architectural patterns would you recommend for dynamically routing user queries to the right data source, ensuring that the final answer meets the user’s expectations? Any best practices or lessons learned would be greatly appreciated.I'm looking for guidance on optimizing our current RAG (Retrieval Augmented Generation) workflow using Azure AI Search. Right now, our system processes a user query by generating search parameters, retrieving context from Azure AI Search, and then combining that with the query to generate a final answer. While this works for some queries, it doesn't fully accommodate the diverse nature of user questions.

For example:

Internal Knowledge Only: A query like "What creative strategies can we explore for a new product launch?" might be answered solely by the model’s internal knowledge.

Company-Specific Data: A query such as "What are the current trends in our industry based on internal performance data?" should tap into a vector database containing company-specific insights.

External Web Search: A question like "What are the latest market trends in healthcare technology?" requires the most current external data from a web search.

Integrated Business Strategy: For a more complex case, consider a query like "Based on the XY business model, how can we create a comprehensive business strategy or marketing campaign?" This would require the model to understand the business model, consider company-specific data, and integrate external insights to deliver a tailored strategy.

My thought is to enhance the current RAG setup by first breaking down the query into subtasks—where the model generates an initial answer, then enriches that answer by retrieving additional context from the most appropriate data source (whether internal, company-specific, or external), and finally synthesizes a richer final output.

I’m considering two approaches:

Multiple Agents: Each specialized in a different part of the process.

Dynamic Routing in Code: Using an LLM to analyze and decompose the query, decide on the optimal data source, and orchestrate the retrieval and synthesis steps.

Has anyone implemented a similar multi-faceted workflow? What strategies or architectural patterns would you recommend for dynamically routing user queries to the right data source, ensuring that the final answer meets the user’s expectations? Any best practices or lessons learned would be greatly appreciated.

Answer 1

Hi there Arun Lal

Thanks for using QandA platform

Optimizing a RAG pipeline with dynamic query routing can defnmitrly enhance response accuracy by ensuring queries are directed to the most relevant data sources. approach involves query classification and decomposition, where an LLM analyzes the query to determine whether it requires internal knowledge, company-specific data, or external sources. For complex queries, breaking them into subtasks ensures each part is processed correctly. Implementing a multi-agent architecture can help. where different retrieval agents specialize in various data sources, such as vector search for internal documents, SQL for structured company data, or web search for external knowledge.

A coordinator agent can then determine which agents to invoke. Dynamic routing and context enrichment further refine responses by retrieving information iteratively, making sure only relevant context is passed to the model. Using an orchestration layer like LangChain can automate this process efficiently. Finally, knowledge fusion ensures the retrieved data is synthesized coherently, possibly using reranking models to prioritize relevant information. Best practices - implementing caching to optimize repeated queries, leveraging vector embeddings for better retrieval accuracy, and experimenting with multi-step prompting techniques. Have you considered a hybrid approach where retrieval and synthesis happen iteratively for improved context integration?

If this helps kindly accept the answer thanks much.