Introduction to LLMOps with an Azure OpenAI Chatbot

Welcome to our deep dive into the world of LLM Ops! In this blog, we’ll explore the key takeaways from a recent webinar hosted by Brian Neelson, an IT professional with nearly 30 years of experience. Brian guided us through building prompt flows using Azure AI Studio, focusing on practical applications and evaluations. This guide is perfect for anyone looking to enhance their skills with Azure AI training.

Introduction to LLM Ops

LLM Ops, or Large Language Model Operations, is an emerging field that focuses on the deployment, management, and optimization of large language models. In this webinar, Brian demonstrated two primary examples of prompt flows, showcasing the capabilities of Azure AI Studio. This is an essential part of Azure AI training for those looking to specialize in AI and machine learning.

Building a Few-Shot Prompt Flow

The first demonstration involved creating a few-shot prompt flow. This technique allows a large language model to learn from a few examples without additional training. By providing several text and label examples, the model can categorize new prompts accurately. This method is particularly useful for tasks that require quick adaptation to new data, making it a valuable skill in Azure AI training.

Custom PDF Prompt Flow

Next, Brian showed how to use a custom PDF file in a prompt flow. This involved creating three additional prompt flows to evaluate the original one for groundedness, relevance, and similarity. These evaluations ensure that the model’s responses are accurate and relevant, especially when incorporating custom data. Learning these techniques is a crucial part of advanced Azure AI training.

Step-by-Step Guide to Azure AI Studio

1. Creating a New Project: Start by visiting Azure AI Studio and creating a new project. This involves setting up a new hub and deploying a base model, such as GPT-4. This foundational step is covered in many Azure AI training programs.
2. Building the Prompt Flow: Use the web classification example to create a prompt flow. This example comes with pre-built few-shot examples and an additional prompt for testing.
3. Configuring the Prompt Flow: Set up the connection and deployment, and provide the necessary prompts and data. This step involves defining the URL, text context, category, and evidence.
4. Running the Prompt Flow: Execute the prompt flow and review the results. Adjust the flow as needed to ensure accurate categorization.

Evaluating Prompt Flows

Brian emphasized the importance of evaluating prompt flows for groundedness, relevance, and similarity. These evaluations help maintain the accuracy and reliability of the model’s responses. The process involves:

1. Groundedness Evaluation: Ensuring the model’s responses are based on solid evidence.
2. Relevance Evaluation: Checking that the responses are pertinent to the given prompts.
3. Similarity Evaluation: Verifying that the responses are consistent with similar prompts.

Conclusion

The webinar provided a thorough overview of LLM Ops and practical insights into using Azure AI Studio for building and evaluating prompt flows. By following these steps, you can harness the power of large language models to create accurate and reliable AI applications. This knowledge is invaluable for anyone undergoing Azure AI training.

For more advanced courses and personalized guidance, reach out to Obility. They offer comprehensive Azure AI training to help you master LLM Ops and integrate it into your organization effectively.