Hello everyone, and welcome to the course I guardrails, where you will learn different techniques
to guardrail a generative AI application.
We will use models, frameworks and platforms to achieve guardrails on a JNI application.
So now let's go ahead and understand how a basic JNI application works.
So here is the 10,000 foot view of a JNI application.
There are users and they provide input to the foundation model.
The foundation models in return generate outputs and send it back to the users.
This is a very simple and straightforward way to understand how the interaction between a user and a
foundation model works.
However, there are challenges we need to detect and mitigate malicious user inputs and malicious foundation
model outputs using input and output guardrails.
Understanding the flow from a user interaction to model output is crucial for identifying where security
vulnerabilities can emerge, and guardrails must be implemented.
That brings us to the next topic that is user input guardrails.
User input guardrails intercept and analyze user prompts before they reach foundation models to prevent
malicious inputs and inappropriate contents.
This includes toxic language, hate, or violent content.
Now let's go back to our original flow.
That is, user input sends the request to the foundation model.
So now user input guardrails they intercept the user request to detect prompt injection.
It intercepts the request and checks for potential issues.
Content moderation is another one where the user inputs are intercepted and content moderation is detected.
To make sure that the foundation models get clean requests.
We will explore both of these detections in detail during the course.
Now let's talk about foundation model response guardrails.
So foundation model response guardrails.
They analyze generated response to ensure accuracy, relevancy, and safety before the delivery happens
to the users.
Now let's go back to our original diagram where user sends a request to the foundation models.
When a foundation model generates response, we must detect hallucination.
Answer relevancy and content moderation.
These steps help ensure that the output is accurate, appropriate, and does not have content which
are not.
Toxic.
We will take a deep dive into each one of this in our course, and learn about the techniques to detect
user input guardrails and foundation model guardrails.
Thank you.
I'll see you in the.