Microsoft's Copilot Stack, introduced at Microsoft Build2023, offers a suite of services and code frameworks that enable the integration of AI language models, like ChatGPT, into business applications. This integration represents a paradigm shift in utilizing the information available to enterprises. The Copilot Stack includes conversational user interfaces that enhance user experiences, orchestration frameworks like Semantic Kernel for seamless communication, and retrieval augmented generation (RAG) for accessing relevant information and improving responses. The AI infrastructure powering the Copilot Stack is backed by advanced NVIDIA GPUs, ensuring efficient deep-learning model execution.
In an era where AI is disrupting entire industries, the integration of AI language models into business applications represents a paradigm shift in how enterprises harness the wealth of information at their disposal. Microsoft’s Copilot Stack, unveiled at Microsoft Build 2023, serves as a catalyst in this transformative journey. This powerful suite, which encompasses a range of technological services and code frameworks, enables developers to rapidly deploy AI language models, thereby augmenting applications with unprecedented capabilities. Whether it’s streamlining workflows, enhancing user experiences, or deriving insights from data, the integration of AI language models like ChatGPT can be catalytic enablers for your business. Before we delve into the Copilot Stack, if you are new to Large Language Models, we recommend acquainting yourself with the fundamentals through our introductory
The Future of Competition: Harnessing AI Language Models for Competitive Edge
For the remainder of this blog, we will dissect the Copilot Stack:
Traditional user interfaces typically include buttons, input fields, and menus. However, with AI language models, these should be complemented with conversational interfaces. This becomes especially powerful for accomplishing complex tasks requiring navigating multiple screens, searching or executing numerous commands. Users can interact with an intelligent agent capable of efficiently completing tasks using conversational language. And this is how we naturally interact with our world. With the rising popularity of AI tools like ChatGPT, users are increasingly seeking chat windows to interact with applications, marking a fundamental shift in user experience.
Microsoft's open-source framework, Semantic Kernel, plays a critical role by facilitating communication between the underlying foundation models and the user. This ensures that the interaction is seamless and efficient. The orchestration layer has several sub-components:
ChatGPT (and other AI language models) operate based on prompts, where the user provides input, and the model generates a response. You don't want your business users enabled to ask any questions they want (if you do, they should use ChatGPT), so the prompt & response filtering layer controls what questions and answers your application allows. It also applies a moderation filter to ensure that the prompts meet safety guidelines and do not elicit irrelevant or unsafe responses from the model.
Tis layer allows you to interject standard prompt instructions to the language model. For example, you might create a role prompt by injecting "act as a legal expert and respond to legal questions" into the prompt. The stack then analyzes the prompt entered by the user to understand the actual intent.
When interacting with advanced AI language models, like ChatGPT, it's remarkable how knowledgeable they seem to be. This prowess is achieved through the use of two sophisticated tools. The first tool, akin to an intelligent dictionary, corresponds to the "grounding layer". It allows AI language models to quickly access and retrieve relevant information, serving as a base for them to stand on. Similar to how we use search engines, AI language models utilize this grounding layer to find pertinent information and make sure that their responses are contextually relevant.
The second tool acts as a bridge and corresponds to the "plugin". It connects AI language models to an extensive repository of information, including databases and various sources on the internet. This plugin is vital for AI language models to access real-time information, enhancing the quality and relevance of their responses.
This industry standard approach Retrieval Augmented Generation, or RAG, is incorporated into the Copliot stack. Think of RAG as an empowering layer that turns AI language models into highly informed personas. By synergizing the grounding layer (the intelligent dictionary) and the plugin (the bridge), RAG ensures that AI language models' responses are not only prompt but also infused with the most current and contextually appropriate information. The next time you find yourself in awe of an AI language model's knowledgeable response, you'll know that there’s a sophisticated set of tools working behind the scenes to ensure that information is accurate and relevant.
As the core of the Copilot Stack, foundation models are trained on extensive datasets and capable of executing a wide array of tasks. And these models can be fine-tuned for specific applications, such as understanding industry-specific terminologies in healthcare, legal, or finance domains.
Training your own large language model on a very large dataset, like ChatGPT, is very expensive and time consuming. For this reason, only the largest technology companies have the resources required to train a LLM. Some of the more common models are:
Off-the-shelf large language models, though powerful, might not always cater to niche domains or specific data types, and they may lack the tailored accuracy required for specialized tasks. Fine-tuning an existing LLM is generally more efficient and practical, especially when resources and data are limited.
By training your large language model, you gain control over the data and fine-tuning processes, ensuring that the model is optimized for the particularities and nuances of your application's domain, enhancing performance and relevance in specific fields, such as healthcare, law, or finance. You also create your own intellectual property (IP).
You should consider using a customized model when you have a combination of the following requirements:
Acting as the backbone of the Copilot Stack, the infrastructure consists of an AI tuned compute that operates on public clouds. This purpose-built infrastructure is driven by tens of thousands of cutting-edge NVIDIA GPUs, furnishing the computational prowess necessary to run sophisticated deep-learning models that swiftly respond to user prompts—notably, this same infrastructure powers ChatGPT, one of the most successful AI applications.
To harness the transformative capabilities of AI for operational efficiency, data-driven insights, or novel services, expert guidance is advised. We invite you to connect with us for a conversation tailored to your business objectives and explore the strategic deployment of AI language models as a competitive differentiator. Seize this opportunity to position your enterprise at the forefront of AI innovation. To explore how our solutions can be tailored to meet your unique requirements, please click the 'Connect' button at the top of this page to schedule a meeting with our team of experts.
