In this exploration, we delve into enhancing Chat Copilot’s functionality by integrating custom plugins, opening up a dialogue between the Semantic Kernel and Chat Copilot. Through a practical example, we see how Chat Copilot can seamlessly interact with the plugin to solve a given AI task. This setup forms a foundation for more complex engagements, bringing closer the intertwined future of coding and AI.
ai
4 posts
In this post, we explore the fusion of Semantic Kernel with the Retrieval Augmented Generation (RAG) pattern, unraveling a new horizon in AI capabilities. By melding Semantic Kernel’s prowess in understanding relationships between words with RAG’s adeptness in fetching real-world data, we aim to foster models that are not only contextually aware but are also grounded in factual accuracy. Dive in to discover how this amalgamation could be a game-changer in generating more insightful and reliable AI responses.
The article elaborates on utilizing Planners with Semantic Kernel to autonomously orchestrate AI tasks based on user requests. It details various Planner types like Basic, Action, Sequential, and Stepwise Planners, explaining their functionalities.
Delve into the world of Semantic Kernel, an innovative SDK by Microsoft, designed to bridge the gap between Large Language Models like OpenAI’s ChatGPT and the diverse software development environments. Explore how Semantic Kernel enhances interaction with LLMs by introducing a structured way to create Plugins and a Planner to manage them. Venture through a practical demonstration of crafting a DevOps Plugin to automate Kubernetes deployment tasks, showcasing the potential of Semantic Kernel in modern software development.