4 Key Considerations for Unlocking the Power of GenAI

Paul Done and Steve Jurczak


Artificial intelligence is evolving at an unprecedented pace, and generative AI (GenAI) is at the forefront of the revolution. GenAI capabilities are vast, ranging from text generation to music and art creation. But what makes GenAI truly unique is its ability to deeply understand context, producing outputs that closely resemble that of humans. It's not just about conversing with intelligent chatbots. GenAI has the potential to transform industries, providing richer user experiences and unlocking new possibilities.

In the coming months and years, we'll witness the emergence of applications that leverage GenAI's power behind the scenes, offering capabilities never before seen. Unlike now popular chatbots like ChatGPT, users won't necessarily realize that GenAI is working in the background. But behind the scenes, these new applications are combining information retrieval and text generation to deliver truly personalized and contextual user experiences in real-time. This process is called retrieval-augmented generation, or RAG for short.

So, how does retrieval-augmented generation (RAG) work, and what role do databases play in this process? Let's delve deeper into the world of GenAI and its database requirements.

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The challenge of training AI foundation models

One of the primary challenges with GenAI is the lack of access to private or proprietary data. AI foundation models, of which large language models (LLMs) are a subset, are typically trained on publicly available data but do not have access to confidential or proprietary information. Even if the data were in the public domain, it might be outdated and irrelevant. LLMs also have limitations in recognizing very recent events or knowledge. Furthermore, without proper guidance, LLMs may produce inaccurate information, which is unacceptable in most situations.

Databases play a crucial role in addressing these challenges. Instead of sending prompts directly to LLMs, applications can use databases to retrieve relevant data and include it in the prompt as context. For example, a banking application could query the user's transaction data from a legacy database, add it to the prompt, and then send this engineered prompt to the LLM. This approach ensures that the LLM generates accurate and up-to-date responses, eliminating the issues of missing data, stale data, and inaccuracies.

Top 4 database considerations for GenAI applications

It won't be easy for businesses to achieve real competitive advantage leveraging GenAI when everyone has access to the same tools and knowledge base. Rather, the key to differentiation will come from layering your own unique proprietary data on top of Generative AI powered by foundation models and LLMs. There are four key considerations organizations should focus on when choosing a database to leverage the full potential of GenAI-powered applications:

  1. Queryability: The database needs to be able to support rich, expressive queries and secondary indexes to enable real-time, context-aware user experiences. This capability ensures data can be retrieved in milliseconds, regardless of the complexity of the query or the size of data stored in the database.
  2. Flexible data model: GenAI applications often require different types and formats of data, referred to as multi-modal data. To accommodate these changing data sets, databases should have a flexible data model that allows for easy onboarding of new data without schema changes, code modifications, or version releases. Multi-modal data can be challenging for relational databases because they're designed to handle structured data, where information is organized into tables with rows and columns, with strict schema rules.
  3. Integrated vector search: GenAI applications may need to perform semantic or similarity queries on different types of data, such as free-form text, audio, or images. Vector embeddings in a vector database enable semantic or similarity queries. Vector embeddings capture the semantic meaning and contextual information of data making them suitable for various tasks like text classification, machine translation, and sentiment analysis. Databases should provide integrated vector search indexing to eliminate the complexity of keeping two separate systems synchronized and ensuring a unified query language for developers.
  4. Scalability: As GenAI applications grow in terms of user base and data size, databases must be able to scale out dynamically to support increasing data volumes and request rates. Native support for scale-out sharding ensures that database limitations aren't blockers to business growth.

The ideal database solution: MongoDB Atlas

MongoDB Atlas is a powerful and versatile platform for handling the unique demands of GenAI. MongoDB uses a powerful query API that makes it easy to work with multi-modal data, enabling developers to deliver more with less code. MongoDB is the most popular document database as rated by developers. Working with documents is easy and intuitive for developers because documents map to objects in object-oriented programming, which are more familiar than the endless rows and tables in relational databases. Flexible schema design allows for the data model to evolve to meet the needs of GenAI use cases, which are inherently multi-modal. By using sharding, Atlas scales out to support large increases in the volume of data and requests that come with GenAI-powered applications.

MongoDB Atlas Vector Search embeds vector search indexing natively so there's no need to maintain two different systems. Atlas keeps Vector Search indexes up to date with the source data constantly. Developers can use a single endpoint and query language to construct queries that combine regular database query filters and vector search filters. This removes friction and provides an environment for developers to prototype and deliver GenAI solutions rapidly.


GenAI is poised to reshape industries and provide innovative solutions across sectors. With the right database solution, GenAI applications can thrive, delivering accurate, context-aware, and dynamic data-driven user experiences that meet the growing demands of today's fast-paced digital landscape. With MongoDB Atlas, organizations can unlock agility, productivity, and growth, providing a competitive edge in the rapidly evolving world of generative AI.

To learn 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.