The arrival of generative AI (genAI) powered by Large Language Models (LLMs) in 2022 has captivated business leaders and everyday consumers due to its revolutionary potential. As the dawn of another new era in technology begins, the gold rush is on to leverage genAI and drive disruption in markets or risk becoming a victim of said disruption. Now, a vast array of vendors is bringing to market genAI enablers and products. This proliferation of fast-followers leaves executives and software developers feeling overwhelmed.The document model — a perfect fit for AI use casesSuccess doesn’t necessarily equate to differentiation, especially when everyone has access to the same tools. In this environment, the key to market differentiation is layering your own unique proprietary data on top of genAI and LLMs. Documents, the underlying data model for MongoDB Atlas, allow you to combine your proprietary data with LLM-powered insights in ways that previous tabular data models couldn’t, unleashing the potential for truly differentiating AI-powered experiences.The way to do this is by transforming your proprietary data — structured and unstructured — into vector embeddings, which capture the semantic meaning and contextual information of data, making them suitable for various tasks like text classification, machine translation, sentiment analysis, and more.With vector embeddings, you can easily unlock a world of possibilities for your AI models. Vector embeddings provide numerical encodings that capture the structure and patterns of your data. This semantically rich representation makes calculations of relationships and similarities between objects a breeze, allowing you to create powerful applications that weren’t possible before.A platform for building with AIMongoDB’s ability to ingest and quickly process customer data from various sources allows organizations to build a unified, real-time view of their customers, which is valuable when powering genAI solutions like chatbot and question-answer (Q-A) customer service experiences. MongoDB Vector Search is a fast and easy way to build semantic search and AI-powered applications by integrating the operational database and vector store in a single, unified, and fully managed platform.Rather than create a tangled web of cut-and-paste technologies for your new AI-driven experiences, the MongoDB Atlas developer data platform provides a streamlined approach to bring those experiences to market quickly and efficiently, simplifying operational and security models, data wrangling, integration work, and data duplication while still keeping costs and risk low.With MongoDB Atlas at the core of your AI-powered applications, you can benefit from a unified platform that combines the best of operational, analytical, and genAI data services for building intelligent, reliable systems designed to stay in sync with the latest developments, scale with user demands, and keep data secure and performant.Real-world AI use casesGradient is an AI company that was founded by former leaders of AI teams at Google, Netflix, and Splunk. The company enables businesses to create high-performing, cost-effective custom AI applications by providing a platform for businesses to build, customize, and deploy bespoke AI solutions. Gradient uses state-of-the-art LLMs and vector embeddings combined with MongoDB Atlas Vector Search for storing, indexing, and retrieving high-dimensional vector data, and LlamaIndex for data integration.Together, Atlas Vector Search and LlamaIndex feed foundation models with up-to-date, proprietary enterprise data in real-time. Gradient designed its platform to use retrieval augmented generation (RAG) — a powerful approach in natural language processing (NLP) that combines information retrieval and text generation — to improve development velocity up to 10x by removing the need for infrastructure, setup, or in-depth knowledge around retrieval architectures.In another example, a nationally ranked medical and surgical facility, Flagler Health, is using sophisticated AI techniques to rapidly process, synthesize, and analyze patient health records to aid physicians in treating patients with advanced pain conditions. This enables medical teams to make well-informed decisions, resulting in improved patient outcomes with an accuracy rate exceeding 90% in identifying and diagnosing patients.As the company built out its offerings, it identified the need to perform similarity searches across patient records to match conditions. Flagler’s engineers identified the need for a vector database but found standalone systems to be inefficient. They decided to use MongoDB Atlas Vector Search. This integrated platform allows the organization to store all data in a single location with a unified interface, facilitating quick access and efficient data querying.To find out more about how Atlas Vector Search enables you to create vector embeddings tailored to your needs (using the machine learning model of your choice, including OpenAI, Hugging Face, and more) and store them securely in Atlas, download our white paper, Embedding Generative AI and Advanced Search into your Apps with MongoDB.
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