AI

When we use the Semantic Kernel SDK, we use plugins that act as the building blocks for our AI applications. Plugins essentially define the tasks that the kernel should complete, and the kernel interfaces with large language models and run the plugins we define. Plugins can include native code and natural language prompts, allowing us to use generative AI in our application. Plugins give us the flexibility of defining desired behavior in our application, and we can create custom prompt plugins to fine tune our applications precisely as we need to. ...

The Semantic Kernel SDK is an open-source SDK that allows developers to integrate large language models (also known as LLMs) in their applications. The Semantic Kernel SDK allows developers to integrate prompts to LLMs and results in their own applications. For example, say we’re developing a booking system for a medical clinic. Instead of creating a LLM from scratch, we can use the Semantic Kernel to use existing LLMs and create an AI agent that can understanding the natural language queries that our patients make, provide recommendations based on their queries, and book them in to appointments. ...

Azure OpenAI provides developers with the ability to add AI to their applications using a variety of different models from OpenAI. This includes GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo and Embedding models. We can add AI functionality to our applications using C#, Python or REST APIs. The Generative AI capabilities that are available in Azure OpenAI are provided through the models, which belong to different families. This article assumes that you already have access to Azure OpenAI. To use Azure OpenAI, you need to be approved. Luckily for me, my work already has a resource for me to use. If you haven’t got one, check out this guide to get started. ...

In Azure AI Search, semantic ranking improves our searches by using language understanding to rerank search results. Semantic search is a collection of query capabilities that improve the quality of search results using text-based queries. Using semantic search we can: Improve Search Results by adding a ranking over initial search results using advanced algorithms that consider the context and meaning of the query, resulting in more relevant search outcomes. Provides Additional Information by extracting and displaying captions and answers from search results, which can be used to improve the user’s search experience. In this article, we’ll talk a little about Semantic Search is in Azure AI Search, its advantages and limitations, how to set it up in Azure AI search, and how we can perform semantic searches using C#. ...

How much text data do you produce on a daily basis? This could be in the form of an email you send to a colleague, or posts you send on social media. You may have gone to a new restaurant and given them a review online, or you could have created some documents such as contracts as part of your work. Now imagine all that data being created on a global scale. How can we apply AI to this vast amount of text data to extract insights from it? ...