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- Enhance Knowledge Management with Llama3 for 10x Performance | Agentic RAG w/ Llama3
Enhance Knowledge Management with Llama3 for 10x Performance | Agentic RAG w/ Llama3
Discover how leveraging Llama3 for knowledge management can revolutionize your information organization and retrieval processes. Learn how a large language model can analyze vast amounts of data to provide personalized answers efficiently.
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Video Transcript
If you ask me what is one use case that clearly AI can provide value,
it's going to be the knowledge management.
No matter which organization you work in,
there are huge amount of wiki, documentation,
and meeting notes that is everywhere and organized no better than a library like this.
It will take forever for any human being to read and digest
all those information and be on top of everything.
But with the power of large language model,
this problem finally is having a solution.
Because we can just get a large model to read
all sorts of different data and retrieve answer for us.
That's why end of last year, there was big discussion about whether search engine like
Google gonna be disrupted by large language model.
Cuz when you have a large language model that has a world knowledge and can provide hyper
personalized answer to you, why do you still want to do the Google search?
And we already start seeing that happen.
There's huge amount of people now go to platform like ChatGPT or Plexity to answer some of
their day to day questions.
And there are also platform like Glean focusing on knowledge management for corporate data.
And as many of you already try, it is actually very easy to.
a AI chatbot that can chat with your PDF, PowerPoint, or spreadsheets.
But if you ever try to build something like that yourself,
you will quickly realize, even though a lot of people think that AI is going to
take over the world, the reality is somewhat different.
Many a time, the AI chatbot you build probably even struggle to answer most
basic questions.
So here's a huge gap between what does the world think AI is capable of today
versus what it's actually capable.
And for the past few months, I've been trying to build different sorts of AI bot
for different business use cases to figure out what is working, what is not.
So today I want to share some of the learning with you.
How can you build a rock application that is actually reliable and accurate?
So for ones who don't know, there are two common ways that you can give large
knowledge model your private knowledge.
One method is fine tuning or training your own model.
You basically bake knowledge into the model weights itself.
So this method can give a large knowledge model precise knowledge
with fast inference because all knowledge already baked into the weights.
The downside is that it is not a common knowledge about how to fine tune a model
effectively because there are so many different parameters.
and you also need to prepare the training data properly.
That's why the other method...
is a lot more common and widely used,
which is you don't really change the model,
but put knowledge into the part of the prompt.
Some people call it in-context learning,
but you might also just refer to it as a RAC,
which represent for retrieval augmented generation.
It basically means instead of getting
the large-language model answer user's question directly,
