How to Avoid GenAI Sprawl and Complexity

Steve Jurczak

Illustration depicting AI. A purple robot has speech bubbles floating around it

There's no doubt that generative AI and large language models (LLMs) are disruptive forces that will continue to transform our industry and economy in profound ways. But there's also something very familiar about the path organizations are taking to tap into GenAI capabilities. It's the same journey that happens anytime there's a need for data that serves a very specific and narrow purpose. We've seen it with search where bolt-on full-text search engines have proliferated, resulting in search-specific domains and expertise required to deploy and maintain them. We've also seen it with time-series data where the need to deliver real-time experiences while solving for intermittent connectivity has resulted in a proliferation of edge-specific solutions for handling time-stamped data. And now we're seeing it with GenAI and LLMs, where niche solutions are emerging for handling the volume and velocity of all the new data that organizations are creating. The challenge for IT decision-makers is finding a way to capitalize on innovative new ways of using and working with data while minimizing the extra expertise, storage, and computing resources required for deploying and maintaining purpose-built solutions.

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Purpose-built cost and complexity

Illustration depicting Search. A magnifying glass is hovering over a check-mark

The process of onboarding search databases illustrates the downstream effects that adding a purpose-built database has on developers. In order to leverage advanced search features like fuzzy search and synonyms, organizations will typically onboard a search-specific solution such as Solr, Elasticsearch, Algolia, and OpenSearch. A dedicated search database is yet another system that requires already scarce IT resources to deploy, manage, and maintain. Niche or purpose-built solutions like these often require technology veterans who can expertly deploy and optimize them. More often than not, it's the responsibility of one person or a small team to figure out how to stand up, configure, and optimize the new search environment as they go along.

Time-series data is another example. The effort it takes to write sync code that resolves conflicts between the mobile device and the back end consumes a significant amount of developer time. On top of that, the work is non-differentiating since users expect to see up-to-date information and not lose data as a result of poorly written conflict-resolution code. So developers are spending precious time on work that is not of strategic importance to the business, nor does it differentiate their product or service from your competition.

The arrival and proliferation of GenAI and LLMs is likely to accelerate new IT investments in order to capitalize on this powerful, game-changing technology. Many of these investments will take the form of dedicated technology resources and developer talent to operationalize. But the last thing tech buyers and developers need is another niche solution that pulls resources away from other strategically important initiatives.

Documents to the rescue

Illustration depicting Vector Search

Leveraging GenAI and LLMs to gain new insights, create new user experiences, and drive new sources of revenue can entail something other than additional architectural sprawl and complexity. Drawing on the powerful document data model and an intuitive API, the MongoDB Atlas developer data platform allows developers to move swiftly and take advantage of fast-paced breakthroughs in GenAI without having to learn new tools or proprietary services. Documents are the perfect vehicle for GenAI feature development because they provide an intuitive and easy-to-understand mapping of data into code objects. Plus, the flexibility they provide enables developers to adapt to ever-changing application requirements, whether it's the addition of new types of data or the implementation of new features. The huge diversity of your typical application data and even vector embeddings of thousands of dimensions can all be handled with documents.

The MongoDB Query API makes developers' lives easier, allowing them to use one unified and consistent system to perform CRUD operations while also taking advantage of more sophisticated features such as keyword and vector search, analytics, and stream processing — all without having to switch between different query languages and drivers, helping to keep your tech stack agile and streamlined.

Making the most out of GenAI

AI-driven innovation is pushing the envelope of what is possible in terms of the user experience — but to find real transformative business value, it must be seamlessly integrated as part of a comprehensive, feature-rich application that moves the needle for companies in meaningful ways.

MongoDB Atlas takes the complexity out of AI-driven projects. Our intuitive developer data platform streamlines the process of bringing new experiences to market quickly and cost-effectively. With Atlas, you can reduce the risk and complexity associated with operational and security models, data wrangling, integration work, and data duplication.

To find out more about how Atlas helps organizations integrate and operationalize GenAI and LLM data, download our white paper, Embedding Generative AI and Advanced Search into your Apps with MongoDB. If you're interested in leveraging generative AI at your organization, reach out to us today and find out how we can help your digital transformation.