Do you still need RAG (Retrieval Augmentation Generation) now that we have Microsoft Copilot Pro?
What is RAG?
Retrieval-Augmented Generation (RAG) is a process that optimizes the output of a large language model (LLM) by referencing an authoritative knowledge base outside of its training data sources. It extends the capabilities of LLMs to specific domains or an organization’s internal knowledge base, without the need to retrain the model.
In the context of Microsoft’s Azure platform, RAG can be used in conjunction with Azure’s various data and AI services. For instance, Azure AI Search, formerly called Azure Cognitive Search, is a cloud-based search-as-a-service solution that provides secure information retrieval at scale over user-owned content in traditional and conversational search applications.
Azure AI Search supports vector and text search, AI enrichment, query syntax, and integration with Azure AI services and Azure OpenAI. This makes it a powerful tool for implementing RAG, as it provides the necessary infrastructure for storing and retrieving the external knowledge base.
Azure AI Search can integrate with a variety of data sources. It can pull in content from other Azure services using indexers and the following data source connectors: Azure Blob Storage, Azure Cosmos DB for NoSQL, Azure SQL Database, Azure Table Storage, and Azure Data Lake Storage Gen2. This flexibility allows organizations to leverage their existing data infrastructure when implementing RAG.
Indexing is a crucial part of the data integration process. In Azure AI Search, indexing is an intake process that loads content into your search service and makes it searchable. Internally, inbound text is processed into tokens and stored in inverted indexes, and inbound vectors are stored in vector indexes.
Vector search is a new capability for indexing, storing, and retrieving vector embeddings from a search index. By representing text as vectors, vector search can identify the most similar documents based on their proximity in a vector space. This feature enhances the capabilities of Azure AI Search, making it a powerful tool for implementing RAG.
Despite these technical requirements, RAG continues to be a valuable tool in the AI toolkit. Its ability to ground the responses of an LLM in verified information makes it an important asset for applications where accuracy and authority are paramount.
Microsoft Copilot Pro
Microsoft Copilot Pro is a standalone AI chatbot integrated with Bing search. It offers conversational responses, image generation, logical reasoning, and data organization. With a monthly subscription, users gain priority access to the latest GPT-4 and GPT-4 Turbo releases, unlocking Copilot in various applications to enhance productivity.
Unlike RAG, which requires substantial technical knowledge and developer resources, Copilot Pro is designed to be user-friendly and accessible. It’s developed for power users, such as researchers, programmers, and content creators, who may already be relying on the AI program for work projects.
Copilot Pro integrates with Microsoft 365 apps. This allows users to use Copilot in apps including Word, Excel, Outlook, PowerPoint, and OneNote to create, edit, and communicate faster. This seamless integration makes it easy for users to leverage the power of AI in their everyday tasks.
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In addition to its integration with Microsoft 365, Copilot Pro also offers advanced features such as priority access to GPT-4 and GPT-4 Turbo. These advanced AI models offer improved performance and capabilities, making Copilot Pro a powerful tool for a wide range of tasks.
Despite its advanced features, Copilot Pro is designed to be easy to use. Its user-friendly interface and intuitive controls make it accessible to users of all skill levels. Whether you’re a seasoned programmer or a novice user, Copilot Pro can help you harness the power of AI in your work.
Microsoft Copilot Pro is more than just an AI tool; it’s an AI companion. It’s designed to work alongside users, assisting them in their tasks and helping to enhance their productivity. With Copilot Pro, users can focus on their work, while the AI handles the repetitive tasks and provides helpful suggestions and insights.
Is RAG going to be obsolete?
While it’s natural to question the relevance of RAG in the face of newer AI tools like Microsoft Copilot Pro, it’s important to understand that obsolescence in technology isn’t a simple matter of one tool replacing another.
In my perspective, RAG and other enhancements with LLMs will still be relevant but more on the development perspective. This means when creating bespoke and business solution specific “AI infused” apps that you want to serve to customers, RAG is still the viable option at the moment. It is going to be niche and would need a constant evaluation on the need versus the wants.
Microsoft Copilot Pro on the other hand is driven more on the SaaS model to let consumers work with “their data”. It’s the low hanging fruit for organisations to get started straight away. One analogy I can think of is a CRM system. Although there are ways to create your own custom CRM, there are already battle tested CRM platforms that you can use straight away. However, for apps that does more custom features beyond the CRM, that’s where more development and platforming effort comes in to play.
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Your insight on the evolving role of RAG in the era of Copilot Pro is quite perceptive! 🤓 Generative AI can indeed refine the quality of your work, allowing you to focus on the unique aspects that require your expert touch. By booking a call with us, we can explore how generative AI can streamline your workflow and enhance your productivity, ensuring you get the best of both tools. 🚀 Let's unlock the full potential of your projects together! 🌟 Christine