RAG Routing Explained: LLM vs Semantic Router (When to Use What)
RAG Routing Explained: LLM Router vs Semantic Router for Multi-Source Data
Your RAG system works great… until your data isn’t in one place.
Docs → vector database
Structured data → SQL
Relationships → graph DB
Real-time info → APIs
But most RAG systems still do this:
Send every query to the vector store
And the moment someone asks:
"What’s our revenue last quarter?” → it breaks.
This is where RAG Routing becomes critical.
Instead of treating all questions the same, you route each query to the right data source based on intent.
That’s how production AI systems actually work.
In this video, we break down the two main routing approaches:
LLM Routers → use a prompt to classify the query and decide where it should go
Semantic Routers → use embeddings to match the query with the right route (faster, cheaper, no LLM call)
And more importantly—when to use each one.
What You’ll Learn
Why single-source RAG falls apart at scale
How LLM routers classify user intent (with examples)
How semantic routing works using embeddings
Real routing examples:
Vector DB (unstructured docs)
SQL (structured queries like revenue, metrics)
APIs / tools (real-time actions)
Trade-offs between LLM vs semantic routing:
Accuracy
Latency
Cost
Chapters
0:00 The multi-source data problem
0:45 LLM router walkthrough
2:10 Semantic router walkthrough
3:00 When to use which
Key Insight
Not every question should go to your vector database.
Wrong route → wrong answer
Right route → fast, accurate response
Routing is what turns a basic RAG system into a production-ready AI system.
Resources & Links
👉 Full course (AI Coding for PMs):
https://maven.com/rajeshpeko/idea2prod
👉 Free weekly sessions:
https://maven.com/rajeshpeko#lightnin...
👉 Instructor (Rajesh P):
/ rajeshpeko
How are you handling multiple data sources right now?
Are you routing queries… or sending everything to one place?
#RAG #LLM #AIEngineering #AIPM #GenerativeAI #AIProducts #VectorDatabase #Startups #BuildInPublic