You Ask, I Answer: Replacing Fine Tuned Models With RAG?

In today's episode, Larry asks Can Rag Search, retrieval augmented generation, entirely replace the need for fine-tuned models on enterprise knowledge bases. Okay, so this is an enterprise um AI question. The short answer is yes. So a RAG system, retrieval augmented generation, is how machines can quickly index and prepare a database for retrieval, hence the name retrieval augmented generation. It uses a series of techniques called vectorization and embedding that effectively provides, think of it this way: if if an AI model is like a library, uh, a rag database, a vector database, is kind of like putting an addition on the library.

And the addition on the library is all your stuff. And AI is told use this first before using your own internal knowledge. It is a very effective way to get a machine to understand who you are and what you do. And Larry's question is: is it better than fine-tuning? In the early days of AI, companies uh would fine-tune a model.

They take a model like you know the original Meta Llama model, and they would essentially retrain it with things like reinforcement learning to learn new knowledge, to basically rearrange the public library. Uh, you know, if you could imagine making a copy of a public library and putting your own books in it and trying to get the public library to be your company library. That turned out to be fairly inefficient, as you can imagine, um, very expensive, and went out of date very, very, very quickly. The current best practice is a hybrid database of three different technologies in combination. Again, this is enterprise AI, so you're talking very large data sets, you're talking very large companies, you're talking very large company budgets who can afford to do this sort of thing.

And the three technologies are retrieval augmented generation, which uses that vector system that I was talking about, where you basically make a new version of the model in a sense as an addition on the library, combined with hybrid structured databases. So this is something like uh PostgresQL uh or BigQuery or what have you. Any database can handle having a combination of both structured data and vectors. So imagine that new branch, the library, has two different card catalogs. One is your traditional Dewey Decimal, one is your ISBN, right?

Your library of Congress numbers. Um, the machines work with ISBN, the humans work with Dewey Decimal, and both index the same data, but they do it in different ways. And so the machine, because it can remember an ISBN number and Library of Congress numbers, and we can't. We remember Dewey Decimal much better because they're shorter numbers, they do a better job. Each one does a good job of getting to the knowledge, and then the third system, which is sort of a meta layer on top of this that you see, particularly with systems like Microsoft Copilot, or for the enterprise version of Copilot, is um knowledge graphs.

So Microsoft knowledge graph, which runs as part of Copilot Premium. Um any of these knowledge graphs, Neo4J and Graph Networks, etc., that provide a fast way to get through the card catalog. Imagine that there's a you know, that's the computer system that searches the card catalog, those three combinations. So you have vector database, structured database, uh, and then knowledge graph that's just on top of it, is how modern enterprises today are handling their data with AI. And if you don't have those systems, it gets very hard to use your data with AI, and the risks of exploding token budgets and exploding costs gets very, very high.

Because think about it. In an enterprise with enterprise AI, in an enterprise, you probably have hundreds of thousands, if not millions, of documents. You certainly have millions of emails and other things all over the place, and you have different kinds of access controls and security lists. And if you are, if you've done a good job with your knowledge management, and now we're talking about stuff that goes back to like the 90s, then it's very easy to let AI work with your knowledge management system. That's why Microsoft, for example, has something like Knowledge Graph, because it's it's a good complement to the data that you already have.

If, on the other hand, you haven't done a good job with your enterprise knowledge management, then it's going to be very difficult for AI to work with that. And your AI costs will be very, very high as AI tries to you know route through and search through all sorts of things that maybe uh aren't the best use of its time, certainly not the best use of its budget, and potentially can lead to situations like has happened at many companies where an AI system has gotten access to things that it maybe shouldn't have for everybody, like payroll data. Good AI governance, knowledge graphs, retrieval augmented generation as part of a hybrid database is the way to handle that because then it adheres to and obeys your existing structure of permissions and access control. You don't have to worry about it having access to data it shouldn't, because it's going to use the existing systems as they are. That's one of the things that even from early days set Anthropic apart from other providers.

Is most other providers say, bring your data to us and we'll process it. Uh open AI, for example, is like that. Anthropic says, No, we'll bring our models to your infrastructure and work in inside of them. And that is a bit more privacy-friendly. But at the end of the day, those are the systems.

You do not need to be fine-tuning models anymore. For the most part, for most applications in most places, that is not a good use of AI resources in the enterprise. Most enterprise AI benefits most from the hybrid database, the knowledge graph, um, and uh the the vectors and the structured data within the hybrid database, combined with a good system like I can't believe I'm gonna say this, but combined with something like SharePoint, where there's both human accessible and machine accessible data. It's the same data, but it's structured differently. Thanks for the question.

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