How we differ
How do you differ from other platforms such as NotebookLM, or just plain LLM models?
That's an excellent question! We are not an answers chatbot—we prepare your corpus so any LLM you choose answers better.
RAG stands for Retrieval-Augmented Generation. It is a way for AI to find real information and use it to give better answers.
There are many ways to accomplish this.
The most prominent difference is a technical one. In simple terms, if information was a huge field of books laying in the dark:
Figurative explanation
Pros
Cons
Plain LLMs would use a single powerful light projector to shine on a large area of the field (called a "context window") and thus illuminate that area (get the data from it).
Able to ingest a lot of data at once. Great for use cases where all the data can be held in the model's context window.
When the model's context window can't hold all the data, it will start "fogetting" things.
The common RAG approach uses powerful lasers to shine on specific areas and thus illuminate them (get the data from them).
Able to reach any point in the data where there is information similar to what the query is talking about.
May miss hidden insights or unknown information if the user isn't sure what to look for exactly.
Our proprietary approach uses numerous small agents, each with a small flashlight, to plow through the entire field bit by bit (not just specific areas).
Able to find any and all hidden insights in the information, relevant to the query, either explicitly or implicitly.
Slower. It may take a few minutes to get the results, depending on the size of the data. However, there are ways to speed it up (see smart snapshots).