This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Community Hub.

I'm Shivam Goyal, a dedicated Microsoft Learn Student Ambassador with a deep passion for the fields of Artificial Intelligence and Machine Learning. I am continuously amazed by the power of AI in solving intricate problems and making our lives more convenient. In this blog, you will follow me on a journey through the development and management of AI models, exploring topics such as Contoso Chat, Prompt Engineering, Large Language Models (LLM Ops), and more. Let's dive in!

The field of artificial intelligence (AI) is constantly evolving, reshaping our relationship with technology. A key illustration of this transformation is Contoso Chat, a conceptual application that serves as an AI-powered communication platform. Chat applications have significantly evolved, and Contoso Chat embodies this advancement. The shift from a simple chat interface to an intricate Large Language Model (LLM) operation underscores this thrilling change.

Pre-requisites

• Azure for Student Subscription - Signup for a free account.
• Visual Studio Code - Download Visual Studio Code - Mac, Linux, Windows
• GitHub Education Account - Join GitHub · GitHub
• Access to Azure Open AI Services - Limited access to Azure OpenAI Service - Azure AI services | Microsoft Learn
• Microsoft AI Studio - https://ai.azure.com 

Key Concepts

• Concept of Contoso Chat: Contoso Chat is a hypothetical real-time communication application used for technical demonstrations. It serves as a blueprint for similar application development.

  • GitHub - Azure-Samples/contoso-chat: This sample has the full End2End process of creating RAG application with Prompt Flow and AI Studio
  • Completely Build Contoso Chat: This article offers a thorough end-to-end reference example for creating a copilot application from start to finish using Azure AI Studio with Prompt Flow.

• Prompt Engineering: This process involves crafting effective prompts for AI models to enhance their performance. It directly influences the accuracy of AI responses.

  • Apply prompt engineering with Azure OpenAI Service - Training | Microsoft Learn
  • 15 Tips to Become a Better Prompt Engineer with Generative AI (microsoft.com)
  • One location to get 14 excellent free resources about prompt engineering: A list of free resources on prompt engineering is provided in this post.

• Limitation of Prompt Engineering: These are the constraints in Prompt Engineering, such as controlling AI's detailed responses or handling complex queries, affecting the usability of AI.

  • What is prompt engineering?: This article discusses some challenges and limitations related to adversarial prompting, factuality, and biases.
  • Prompt engineering techniques with Azure OpenAI - Azure OpenAI Service | Microsoft Learn

• Large Language Model Operations (LLM Ops): LLM Ops involve managing and optimizing large language AI models. They directly impact the functioning and performance of AI systems.

  • Introduction To LLM Ops | TechCommunity: This article comprises of introduction to the LLM Ops and managing Large Language Models using Azure ML.
  • Large Language Model Operations (LLMOps) | Coursera: This course offers hands-on projects and best practices in deploying, managing, and optimizing large language models.

• The RAG (Retrieval Augmented Generation) Pattern: RAG improves the depth and quality of AI responses by using external documents during the generation process.

  • Mastering RAG Pattern Chatbots: This article provides a deep dive into the RAG pattern, a sophisticated architecture designed to enhance AI-driven applications.
  • GitHub - Azure/GPT-RAG: This GitHub repository provides resources for understanding and implementing the RAG pattern.
  • Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service - Training | Microsoft Learn

• Azure AI Studio: This is a cloud-based integrated environment for building, training, and deploying AI models, offering a full suite of AI development tools.

  • Beginner's Guide to Azure AI Studio: Developing and Deploying AI Applications : The blog post is a comprehensive guide to getting started with Azure AI Studio, covering its features, steps to start your first AI project, and how to manage and deploy AI models

• Benefits of LLM Ops: The key advantages are improved efficiency, better resource utilization, and enhanced performance of AI models, leading to cost savings and improved experiences.

  • Elevate Your LLM Applications to Production via LLMOps - Microsoft Community Hub
  • An Introduction to LLMOps: Operationalizing and Managing Large Language Models using Azure ML: This blog post discusses the benefits of LLMOps, including efficiency, scalability, and risk reduction.

Additional Resources

• Retrieval Augmented Generation (RAG) in Azure AI Search | Microsoft Learn
• GitHub - Azure-Samples/contoso-real-estate: Intelligent enterprise-grade reference architecture for JavaScript, featuring OpenAI integration, Azure Developer CLI template and Playwright tests.
• Fuel your Intelligent Apps With Azure AI
• GitHub - microsoft/LMOps: General technology for enabling AI capabilities w/ LLMs and MLLMs
• RAG

Conclusion

In conclusion, understanding the concept of Contoso Chat, the process and limitations of Prompt Engineering, the role of Large Language Model Operations (LLM Ops), the RAG Pattern, and the utility of Azure AI Studio, is fundamental to the effective development and management of AI models. These key concepts offer necessary insights into improving the depth, quality, and efficiency of AI responses, thereby enhancing the overall performance of AI systems.