DevOps Engineer, Mentor, budding AI developer

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:

• Store text embeddings in ChromaDB (an open source vector database)
• Retrieve relevant knowledge dynamically
• Use a local model to generate answers

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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.

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Previosuly I've looked at the use of white hat attacks. These test the models as you're training them and should be considered as part of the development process. A prompt injection attack is a type of attack that is used to manipulate the output of a model and is usually an attack of a deployed model.

Large language models generate responses based on user inputs and hidden system instructions. A prompt injection attack exploits this by tricking the AI into ignoring its original constraints, leading to unsafe, unintended, or malicious outputs.

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