Improving AI Apps with MongoDB Atlas – Technology Record

Rebecca Gibson |
November 13, 2024

Building a useful generative artificial intelligence-based application is a challenge. Take, for example, the data orchestration required to create an app that lets consumers take an image of a house and ask an AI-powered search engine to find similar houses in a certain price range and in a specific location.

“The app should bring together geospatial data about the location of the house, vectors of the house’s images so that the AI ​​can analyze its physical features and find similar houses, and price data,” said Abhinav Mehla, regional vice president from cloud. affiliate programs at MongoDB, a developer data platform provider.

To provide factually correct and contextually relevant answers, developers should create Retrieval Augmented Generation (RAG) pipelines that facilitate access to real-time data from multiple sources.

“RAG provides a secure and streamlined way to extend large language models (LLMs) with enterprise data to customize their output and provide accurate answers, while reducing hallucinations and data leaks,” says Mehla. “It creates more informed chatbots and improves search and recommendation engines.”

The easiest, most cost-efficient, and secure way for developers to build production-ready generative AI apps and RAG pipelines is to use a solution like MongoDB Atlas on Microsoft Azure, which unites native vector embedding with live app data into one complete managed, secure, multi-cloud platform.

“MongoDB Atlas covers all transactional, search and retrieval, in-app analytics, geospatial and streaming workload needs so developers don’t have to purchase a separate vector database,” says Mehla. “The LLM has access to any type of live data to extend generative AI models with cutting-edge business truths.”

MongoDB Atlas, Microsoft Azure AI Studio, and Microsoft Fabric provide organizations with an integrated, scalable, and secure platform to bring their data to services like Power BI and Azure OpenAI. “This allows them to leverage the best of AI, machine learning and analytics,” says Mehla. “Developers can also quickly respond to new app requirements and integrate the latest AI innovations. These capabilities are why respondents to an independent AI survey gave MongoDB Atlas Vector Search the highest net promoter score of any comparable solution within months of release.”

To further accelerate the development and large-scale deployment of generative AI apps, organizations can use the MongoDB AI Applications Program (MAAP), which provides validated reference architectures optimized for different use cases. It also offers an end-to-end technology stack that can be securely integrated with solutions from providers like Microsoft.

“MAAP provides customers with the technology, partners and expertise they need to tackle any AI use case and quickly realize a return on their investment,” said Mehla. “We have tested the components through hundreds of customer deployments and know they are secure, scalable, and work well together as a complete solution. MAPP is continuously improved to help customers create generative AI apps that drive immediate value for their teams and customers.”

Deploy MongoDB Atlas on Azure on: bit.ly/3AV9ZSk

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