I’m a DevOps Team Lead based in Australia. I'm passionate about building strong, sustainable teams and helping them grow. I'm excited to share my journey in AI development with a focus on security.
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As we previously went through, a common pattern when implementing models into systems is to use RAG (retrieval augmented generation) by using domain specific data with the GenAI models. But what happens if the data source is compromised or poisoned? In this post we'll explore RAG poisoning attacks, their real-world implications and mitigation strategies to secure your AI implementations.
In this lab, I'm going to move away from security for a little while and look at how we can augment our LLM using a technique called Retrieval Augmented Generation (RAG).
The idea is that we can use a pre-trained LLM to answer questions about a specific dataset. We can then augment the pre-trained model with additional information from a knowledge base. To do this we usually convert the data into a vector space and storing this information in a vector database, like ChromaDB. In this we'll cover:
The main interface to the models is a chat interface. When you present a questions or insutrction, how do the models "know" what you mean? How does it understand the relationship between words?
The answer is embeddings!
An embedding is a high-dimensional vector representation of words, phrases or concepts. Instead of memorising words models map them into a numerical space where similar meanings are close together.