Advanced Rag Techniques- Elevating LLM Interactions with Intelligent Routing | by Guarav Bansal | May, 2025

RAG works by:

1. Retrieval: Given a person question, it retrieves related paperwork or snippets from a information base (e.g., vector database, conventional database, inner paperwork).
2. Augmentation: It then augments the person’s question with this retrieved context and sends it to the LLM.
3. Technology: The LLM generates a response primarily based on the augmented immediate.

Now, think about a RAG system with out routing. It will seemingly:

• Search all out there knowledge sources for each question: Inefficient, sluggish, and doubtlessly overwhelming the LLM with irrelevant context.
• Use a single, generic retrieval technique: Not optimized for several types of questions (e.g., factual vs. analytical).
• Wrestle with advanced, multi-faceted queries: Unable to interrupt down the question and goal particular info.

So Clever Routing will help navigate to the proper knowledge supply. We broadly categorize them into two class:

Drawback

An organization might need a number of information bases:

1. One for HR insurance policies.
2. One other for product specs.
3. A 3rd for buyer help FAQs.
4. A fourth for authorized paperwork.

Routing Resolution

As an alternative of looking out all of them, a logical router (usually powered by a smaller LLM or rule-based system) analyzes the incoming question and decides which information base is most related.

Instance: If a person asks “What’s our firm’s trip coverage?”, the router directs the RAG system to question the HR coverage database, ignoring product specs.

https://python.langchain.com/v0.1/docs/use_cases/query_analysis/techniques/routing/2. Semantic Routing

https://python.langchain.com/docs/concepts/structured_outputs/

Drawback

This system is useful after we wish to redirect the to proper supply primarily based on person’s intent. A easy key phrase search could be advantageous for some queries, however a deep semantic search is required for others.

Routing Resolution

Routing can determine the nature of the person’s question after which choose probably the most applicable retrieval technique and even the precise configuration of the retriever.

Instance:

• Consumer asks “Summarize the important thing findings of the Q3 earnings report.” -> Path to a retriever optimized for extracting and summarizing key factors from monetary paperwork.
• or choose immediate primarily based on customers query

https://python.langchain.com/v0.1/docs/expression_language/how_to/routing/