{"id":29618,"date":"2023-09-19T09:00:00","date_gmt":"2023-09-19T16:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/en-us\/blog\/?p=29618"},"modified":"2025-06-12T09:43:09","modified_gmt":"2025-06-12T16:43:09","slug":"how-we-interact-with-information-the-new-era-of-search","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/how-we-interact-with-information-the-new-era-of-search\/","title":{"rendered":"How we interact with information: The new era of search"},"content":{"rendered":"\n

In today’s rapidly evolving technological landscape, generative AI<\/a>, and especially Large Language Models (LLMs), are ushering in a significant inflection point. These models stand at the forefront of change, reshaping how we interact with information.<\/p>\n\n\n\n

The utilization of LLMs for content consumption and generation holds immense promises for businesses. They have the potential to automate content creation, enhance content quality, diversify content offerings, and even personalize content. This is an inflection point and great opportunity to discover innovative ways to accelerate your business’s potential; explore the transformative impact and shape your business strategy today.<\/p>\n\n\n\n

LLMs are finding practical applications in various domains. Take, for example, Microsoft 365 Copilot\u2014a recent innovation<\/a> aiming to reinvent productivity for businesses by simplifying interactions with data. It makes data more accessible and comprehensible by summarizing email threads in Microsoft Outlook, highlighting key discussion points, suggesting action items in MicrosoftTeams, and enabling users to automate tasks and create chatbots in Microsoft Power Platform<\/a>.<\/p>\n\n\n\n

Data from GitHub demonstrates the tangible benefits of Github Copilot<\/a>, with 88 percent of developers reporting increased productivity and 73 percent reporting less time spent searching for information or examples.<\/p>\n\n\n\n

Transforming how we search<\/h2>\n\n\n\n

Remember the days when we typed keywords into search bars and had to click on several links to get the information we needed?<\/p>\n\n\n\n

Today, search engines like Bing <\/a>are changing the game. Instead of providing a lengthy list of links, they intelligently interpret your question and source from various corners of the internet. What\u2019s more, they present the information in a clear and concise manner, complete with sources.<\/p>\n\n\n\n

The shift in online search is making the process more user-friendly and helpful. We are moving from endless lists of links towards direct, easy-to-understand answers. The way we search online has undergone a true evolution.<\/p>\n\n\n\n

Now, imagine the transformative impact if businesses could search, navigate, and analyze their internal data with a similar level of ease and efficiency. This new paradigm would enable employees to swiftly access corporate knowledge and harness the power of enterprise data. This architectural pattern is known as Retrieval Augmented Generation (RAG), a fusion of Azure Cognitive Search<\/a> and Azure OpenAI Service<\/a>\u2014making this streamlined experience possible.<\/p>\n\n\n\n

The rise of LLMs and RAG: Bridging the gap in information access<\/h2>\n\n\n\n

RAG is a natural language processing technique that combines the capabilities of large pre-trained language models with external retrieval or search mechanisms. It introduces external knowledge into the generation process, allowing models to pull in information beyond their initial training.<\/p>\n\n\n\n

Here’s a detailed breakdown of how RAG works:<\/p>\n\n\n\n

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  1. Input: The system receives an input sequence, such as a question that needs an answer.<\/li>\n\n\n\n
  2. Retrieval: Prior to generating a response, the RAG system searches for (or “retrieves”) relevant documents or passages from a predefined corpus. This corpus could encompass any collection of texts containing pertinent information related to the input.<\/li>\n\n\n\n
  3. Augmentation and generation: The retrieved documents merge with the original input to provide context. This combined data is fed into the language model, which generates a response or output.<\/li>\n<\/ol>\n\n\n\n

    RAG can tap into dynamic, up-to-date internal and external data sources<\/a>, and can access and utilize newer information<\/a> without requiring extensive training. The ability to incorporate the latest knowledge leads to better precise, informed, and contextually relevant responses that brings a key advantage.<\/p>\n\n\n\n

    RAG in action: A new era of business productivity<\/h2>\n\n\n\n

    Here are some scenarios where RAG approach can enhance employee productivity:<\/p>\n\n\n\n