The most popular GenAl architecture, RAG, has evolved so much💯👇
All within 2 years and the difference? Night and day.
Here's what actually changed:
Basic RAG (what most people use to build):
(Simplified)
L↳ Chunk documents into pieces
L↳ Store them in a vector database
↳ Retrieve similar chunks via embedding search
L↳ Feed retrieved data to the LLM for a response
Simple. But limited.
The problem with basic RAG:
1. It misses connections between information
2. Can't reason across multiple data sources
3. Retrieves irrelevant context (garbage in = garbage out)
4. No iterative refinement when the first answer is wrong
Agentic RAG (the upgrade):
(Simplified)
Instead of one-shot retrieval, you get:
L, A retrieval agent that reasons iteratively
L↳ Multiple enterprise tools (vector search, web search, SQL)
The agent decides which tool to use when
L↳ Refines its approach until it finds the right answer
This is 2x better for complex queries.
But the problem started once we saw the limitations of single-agent systems.
With a multi-agent system, you have better control as well as the ability to chain multiple tools and reason on each agent's output.
Now, for graph DB, we all know that enterprise documents are related to each other.
Hence, just finding a similarity search was not enough for these documents.
We needed a much more concrete structure in which document relationships are mapped so that they can be retrieved according to those relationships.
That's when we started using
Multi-agent graph RAG (the final evolution):
(Simplified)
Now you have specialized agents:
L, Graph agent: Understands entity relationships
L, SQL agent: Queries structured databases
L↳ Web agent: Pulls real-time information
L↳ Retrieval agent: Orchestrates everything
Each agent is an expert. The retrieval agent coordinates them.
But you cannot afford to build graph RAG when you are just starting.
So, here's the correct sequence:
- Start with basic RAG. Get it working.
- Add agentic capabilities once you hit limitations.
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