4 Key Considerations for Unlocking the Power of GenAI

Paul Done and Steve Jurczak
October 26, 2023 | Updated: August 8, 2024
#genAI

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.

Check out our AI resource page to learn more about building AI-powered apps with MongoDB.

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 Even if the data were in the public domain, it might be outdated and irrelevant.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:

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.
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.
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.
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.

Conclusion

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.

Head over to our quick-start guide to get started with Atlas Vector Search today.

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Config at MongoDB Shares Why Neurodiversity Matters

After a guest speaker from AsIAm visited our Dublin office in 2022, a few MongoDB employees realized there was a need for a global employee resource group (ERG) focused on disability and neurodiversity at MongoDB. This group of employees brainstormed and gathered information for a few months, contributing to a defined strategy for our new ERG, Config (a play on the word “configuration” and a nod to the database industry). Config officially launched in April 2023 with an AsIAm speaking event held in our Dublin office – the same organization that inspired the group’s formation the previous year. Neurodiversity in the workplace Recognizing neurodiversity within the scope of diversity and inclusion initiatives is a crucial aspect of modern workplace diversity efforts. It promotes a more inclusive environment where individuals with diverse neurological profiles, such as autism, ADHD, or dyslexia, feel valued and included in the workplace. Neurodiversity can manifest in various ways and may show up as communication differences, sensory sensitivities, attention to detail, and unique approaches to problem-solving. Some neurodivergent individuals may have challenges with verbal communication or understanding non-verbal cues while others might excel in written communication or have a unique communication style. Sensory sensitivities can bring discomfort in environments with bright lights, loud noises, or strong odors. Also, many neurodivergent individuals thrive on routine and predictability, seeking consistent work schedules and clear expectations. How to create an inclusive environment In regard to recruitment and hiring, actively seeking neurodiverse talent through partnerships with neurodiversity-focused organizations, job boards, and outreach programs contributes to building a diverse workforce. On top of that, it’s important to educate recruiters and interviewers about neurodiversity to ensure they understand and appreciate different cognitive profiles and can create an inclusive interview process. Alternative interview formats can provide a more accurate assessment of a candidate’s abilities, and structured interview questions and clear instructions help to reduce ambiguity during interviews. Allow candidates to choose interview settings that are comfortable for them, such as in-person, video, or written formats. With workplace support, consider establishing mentoring programs to help neurodivergent employees navigate the workplace and build social connections. Work to create a sensory-friendly environment, clear communication, flexibility, and supportive supervision. In helping employees to become more supportive of their neurodivergent peers, create Neurodiversity Training to raise awareness about neurodiversity and promote a more inclusive culture. Peer Support Networks are also helpful for neurodivergent employees to share experiences and advice. Some reasonable accommodations to consider are individual accommodation plans, quiet spaces for those who need a break from sensory stimuli, task modifications, and communication aids. By implementing these accommodations and support structures, employers can create a more inclusive and supportive environment for neurodivergent employees, enabling them to thrive and contribute to their unique talents. Config members share their stories Supporting my neurodiverse family Yair Bar (He/Him), Escalation Manager Four out of my five children are neurodiverse: two have ADHD and two are on the autism spectrum. I've gained a deep understanding of their employment challenges once they become adults, and these barriers often stem from our neurotypical society's lack of understanding, empathy, and flexibility. Workplaces must proactively create inclusive environments for neurodivergent individuals not only to participate but also thrive. My children have taught me valuable life lessons, like resilience and embracing diversity. My experience as a parent and my awareness of neurodiversity have helped me hone my skills including empathy, encouragement, active listening, and helping individuals redefine their vision and goals. I've applied this perspective in my career and value neurodiverse talent on my team. I firmly believe that organizations should prioritize supporting neurodiversity as it aligns with important values like commitment, social mobility, equality, and personal growth. As neurodiverse populations grow, organizations must address their specific needs. My goal is to support those facing neurodiversity challenges, making a real difference in their lives. I aim to raise awareness about neurodiversity within and beyond MongoDB, and I aspire to contribute to a more inclusive and understanding environment where everyone's unique abilities are celebrated. My personal experience with neurodiversity Rachelle Green (She/They), Workplace Coordinator Prior to 2022, I was not familiar with the concept of neurodiversity. It was through social media that I came across the term, sparking a process of self-reflection. I began to analyze my relationships, cognitive processes, organizational strategies, and sense of belonging in different communities and spaces. In 2023, I made a conscious decision to prioritize my mental health and investigate any concerns or suspicions I had. My goal now is to focus on understanding myself better, maintaining my well-being, and fully embracing my neurodiversity. In my journey, I’ve come to appreciate the importance of organizations embracing neurodiversity in their diversity, equity, and inclusion initiatives. By doing so, they not only foster a more inclusive environment but also tap into a wider talent pool that includes individuals with unique perspectives and exceptional abilities. Config at MongoDB creates a space where neurodivergent thinkers like myself can feel valued and supported. I am eager to contribute my skills and experiences to this cause and make a meaningful impact in promoting neurodiversity acceptance and support within the workplace. An ally for the neurodiverse community Boris Subotic (He/Him), Employee Experience Manager In my HR career, I've witnessed the valuable contributions of neurodiverse individuals, including problem-solving skills, attention to detail, and innovative thinking. I'm committed to advocating for neurodiversity at MongoDB, ensuring all employees, regardless of their neurological differences, feel supported and valued. This fosters a culture of acceptance and enhances productivity and innovation. Recognizing neurodiversity aligns with MongoDB's value “Embrace the Power of Differences”. As an HR professional, I aim to be an ally and influencer in shaping company policies and initiatives. I prioritize empathy and understanding, offering mentorship and professional development opportunities. Creating a supportive environment is key to ensuring everyone thrives. I'm enthusiastic about the role of Config in promoting diversity and inclusion. Collaborating with peers and implementing change initiatives can make a significant impact. I'm excited to continue advocating for inclusivity, challenging stereotypes, and unlocking the potential of all employees through Config. We’re committed to creating an inclusive and equitable environment for everyone. Learn more about diversity and inclusion initiatives at MongoDB.

October 25, 2023

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MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024