๐ฅ๐๐ ๐ฆ๐ต๐ผ๐๐น๐ฑ ๐ก๐ฒ๐๐ฒ๐ฟ ๐๐ฒ ๐ฌ๐ผ๐๐ฟ ๐๐ฒ๐ณ๐ฎ๐๐น๐
Stop using Vector RAG as your first option.
I built a production AI tutor for students. The tool lets students upload a photo of a math problem. The AI then explains the solution step by step.
We had a large database of past problems stored as images. I thought using RAG was the obvious choice. The plan was to find the most similar past problem and feed it to the model.
The system failed.
I spent time fixing the retrieval quality. I tested different strategies. I tested different embedding models. Nothing worked.
Vector RAG works by finding semantic similarity. It assumes the most similar item in your database is the one the model needs.
This works for FAQs. It does not work for math.
In my case, similarity was the wrong metric. For math, a student needs a similar solution method. A problem might look similar visually but use a different logic. The images did not contain the logic needed.
Even if the retrieval worked, the model still had to guess the solution method from the example. This added an extra layer of reasoning that caused errors.
We stopped using RAG.
Instead, we turned solution methods into structured knowledge. We let the LLM select the correct method and apply it.
The results improved. The model stopped guessing and started using direct logic.
This change also helped the business. We built a simple interface for the client. Now, non-technical users can edit the knowledge base themselves. They can see results in real time without an engineer.
A knowledge playbook is easier to maintain than a complex RAG stack. It also lowered our infrastructure costs.
Before you build a RAG system, ask one question:
Is the most similar item the same as what the model actually needs?
If the answer is no, find out what the model needs. Give it that instead. Retrieval is a specific design choice. Do not make it your default.
Source: https://dev.to/chou_500/rag-should-never-be-your-default-38lh
Optional learning community: https://t.me/GyaanSetuAi