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<\/h2>\nStudying generative artificial intelligence (AI) systems at the graduate level means learning how to implement, evaluate, and deploy them. Read on to discover the knowledge and skills students actually gain about generative models at Boston University (BU).\u00a0<\/span><\/p>\n In 2025, <\/span>13-year-old Kevin Tang<\/span><\/a> used a large language model (LLM) application programming interface (API) to build a camera monitoring system to protect his grandmother and other elderly people from falls at home. He won $25,000 and the title of “America’s Top Young Scientist” for inventing the FallGuard system.<\/span><\/p>\n Kevin\u2019s feat is impressive, and the patent for FallGuard is pending. He also employed LLM tools to <\/span>establish a website<\/span><\/a> where anyone can download the apps he created for video monitoring for falls at home.\u00a0<\/span><\/p>\n However, FallGuard relied on LLM APIs and existing tools. To take it a step further and build more complex systems in enterprises, a deeper knowledge of model behavior, architecture, evaluation, and deployment is needed.<\/span><\/p>\n Working with a chatbot interface or an API won’t provide the fundamentals of understanding how a generative AI system works \u2014 and how it can be reliable in business or organizational applications. As <\/span>engineering leader Dr. Claire Knight<\/span><\/a> puts it, “You can’t treat LLM models like APIs.” She elaborates, “The same prompt might give you a solid result at 2 p.m. and nonsense at 2:03 p.m.”\u00a0<\/span><\/p>\n You’ve seen references to “<\/span>tokenmaxxing<\/span><\/a>” and leaderboards at different companies that identify “Token Legends” among engineers who process massive amounts of tokens (which are essentially units of data) for their projects. Yet just being able to use large amounts of LLM resources isn’t the best fit for enterprise-level AI development. Students should understand models, tradeoffs, evaluation, and deployment if they want to move into professional building and adapting AI generative models in real-world businesses.<\/span><\/p>\n At Boston University, the <\/span>online Master of Science (MS) in Computer Science & Artificial Intelligence<\/span><\/a> degree covers skills and background necessary to work in production environments for generative AI system design.<\/span><\/p>\n The Generative Models module, CX643, introduces students to the principles and applications that govern generative AI models. You’ll learn core architectures, including:<\/span> In addition to text-based uses, a <\/span>course in generative model architecture<\/span><\/a> covers principles of natural language processing, multimodal AI, and computer vision. This opens doors to a range of potential real-world use cases, from autonomous vehicles to advanced healthcare applications.<\/span><\/p>\n Looking at the diverse uses of AI today, it’s easy to see how transformers and LLMs are just one facet of a multifaceted set of use cases. Graduate courses in generative AI, including the Generative Models module, cover multiple modes and functions within AI model families.<\/span><\/p>\n Although methods of AI implementation are widely varied, <\/span>transformers and LLMs<\/span><\/a> remain central points of access to the technology. Using encoders and decoders, natural language inputs are transformed through massive neural nets to obtain results. Transformers and LLMs are integral to modern generative AI because they support large-scale language understanding and generation.<\/span><\/p>\n Different model families and modes support different use cases and tasks. Graduate study in AI generative models and system design will include VAEs, (GANs), and AI diffusion models, which utilize distinct mathematical methods to expand the horizons of machine learning (ML) creativity.<\/span><\/p>\n Learning about model families and methods of AI implementation provides a foundation for understanding their uses in real life. Still, there’s no replacement for actual implementation.\u00a0<\/span><\/p>\n BU’s course for generative models emphasizes practical implementation. Students learn to evaluate and deploy architectures using industry-standard frameworks. You’ll reinforce skills in responsible AI implementation and real-world techniques.<\/span><\/p>\n You won’t be stuck relying on textbooks or online modules in the course covering generative models. Instead, you can gain experience in not only working with LLMs but also variational autoencoders \u2014 plus knowledge of how to compress data, denoise it, and generate new, synthetic data. Additionally, the course covers GANs and diffusion models as well as LLMs and how to build with them.<\/span><\/p>\n We’ve all heard the statistic that a large percentage of <\/span>AI pilot projects have failed<\/span><\/a> when they are brought to scale in businesses and organizations. Experimental uses of AI remain active and useful, but they are not enough to work in professional environments without the assessment and evaluation that contribute to their effective use.<\/span><\/p>\n When building a generative AI system, you’ll need to rely on your ability to evaluate output quality and track model behavior over time. Another important skill? Assessing how useful the system is in the real world. Practicality is crucial in successful AI model design and implementation.<\/span><\/p>\n Without proper evaluation, AI models can refer to unseen data, cause real-world failures, and exhibit hidden biases. In order for models to perform safely and as designed when performing their assigned tasks, trust and reliability must be demonstrated via rigorous evaluation throughout the model’s lifecycle.<\/span><\/p>\n When it’s time to deploy models after they’ve been developed and initially tested, they will then enter production. This is why the computer science and AI master\u2019s program instills the skills you’ll need to move into the production phase.<\/span><\/p>\n Although models that professionals design may seem to work perfectly while in development, when actual use cases are introduced in production, the situation can change rapidly. This phase of AI model development involves organizational dimensions, including integration into existing business workflows. Security, privacy, and compliance concerns all play a role at this stage.<\/span><\/p>\n The Generative Models module emphasizes deployment, and the following module (Systems Deployment and Responsible Innovation) explicitly prepares students to move AI\/ML solutions from prototyping to production at scale. Issues like responsible use become more critical as systems move into real environments.<\/span><\/p>\n The Generative Models module follows the Mathematical Foundations of Data Science and Applied Machine Learning modules. The next module, Systems Deployment and Responsible Innovation, moves to a later stage of AI systems and model development in the\u00a0<\/span>program’s curriculum<\/span><\/a>.<\/span><\/p>\n The online MS in Computer Science and AI degree at Boston University is designed for those who seek more than surface-level exposure to AI tools. The program prepares learners to design, build, and deploy intelligent systems that perform reliably in production environments.<\/span><\/p>\n The Generative Models module leverages what students learn in the Applied Machine Learning module \u2014 which introduces deep learning, generative models, and advanced neural network topics. You’ll experience hands-on work in Python and frameworks such as scikit-learn and PyTorch through the Applied Machine Learning module.<\/span><\/p>\n BU\u2019s AI sequence for modules 1 through 4 progresses in the following order:<\/span> Gaining a firm grasp of principles underlying AI model development and practical experience in model deployment is essential for <\/span>working professionally<\/span><\/a> in the fast-growing AI sector.<\/span><\/p>\n AI professionals who understand generative AI models at the system level are better prepared for advanced technical work alongside implementation and deployment than those who only know how to use prebuilt tools (like APIs).<\/span><\/p>\n Program graduates build portfolios showing they can design, build, and deploy advanced computing systems in real-world settings. In turn, they emerge <\/span>better prepared for careers<\/span><\/a> and roles such as:<\/span> Explore the <\/span>curriculum<\/span><\/a> for Boston University\u2019s online <\/span>MS in Computer Science and AI<\/span><\/a> to see how the Generative Models module fits into the larger curriculum for generative AI systems in production. This degree supports skills development and mastery of technical workflows along with organizational dimensions \u2014 such as compliance and business integration \u2014 and learning security and privacy techniques to safeguard AI systems and support reliable, well-designed generative AI systems.\u00a0<\/span><\/p>\n Ready to take the next step? Review the <\/span>application requirements<\/span><\/a> and <\/span>frequently asked questions<\/span><\/a> about the program, then <\/span>request more information<\/span><\/a> today.\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" Generative AI Systems: What Students Learn Beyond Using LLM APIs Studying generative artificial intelligence (AI) systems at the graduate level means learning how to implement, evaluate, and deploy them. Read on to discover the knowledge and skills students actually gain about generative models at Boston University (BU).\u00a0 Using an LLM API Is Not the Same […]<\/p>\n","protected":false},"author":17166,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[86],"tags":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/cs\/wp-json\/wp\/v2\/posts\/23767"}],"collection":[{"href":"https:\/\/www.bu.edu\/cs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bu.edu\/cs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cs\/wp-json\/wp\/v2\/users\/17166"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cs\/wp-json\/wp\/v2\/comments?post=23767"}],"version-history":[{"count":1,"href":"https:\/\/www.bu.edu\/cs\/wp-json\/wp\/v2\/posts\/23767\/revisions"}],"predecessor-version":[{"id":23769,"href":"https:\/\/www.bu.edu\/cs\/wp-json\/wp\/v2\/posts\/23767\/revisions\/23769"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/cs\/wp-json\/wp\/v2\/media?parent=23767"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bu.edu\/cs\/wp-json\/wp\/v2\/categories?post=23767"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bu.edu\/cs\/wp-json\/wp\/v2\/tags?post=23767"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Using an LLM API Is Not the Same as Building a Generative AI System<\/b><\/h2>\n
API Use Is Only the Surface Layer<\/b><\/h3>\n
Graduate Study Goes Beyond Consumption to System Design<\/b><\/h3>\n
What Students Study in the Generative Models Module<\/b><\/h2>\n
Core Generative Architectures<\/b><\/h3>\n
\n<\/span><\/p>\n\n
Applications Across NLP, Vision, and Multimodal AI<\/b><\/h3>\n
Generative AI Includes More Than LLMs<\/b><\/h2>\n
Why Transformers Matter<\/b><\/h3>\n
How GANs, VAEs, and Diffusion Models Expand the Field<\/b><\/h3>\n
Implementation Is a Core Part of the Learning Experience<\/b><\/h2>\n
Students Work With Industry-Standard Frameworks<\/b><\/h3>\n
Learning Means Building, Not Just Describing<\/b><\/h3>\n
Evaluation Is What Separates Real Systems Work From Simple Experimentation<\/b><\/h2>\n
Generative Systems Need to Be Assessed, Not Just Run<\/b><\/h3>\n
Why Evaluation Matters for Real-World AI Work<\/b><\/h3>\n
Deployment Is Part of the Skill Set, Too<\/b><\/h2>\n
Generative Models Need More Than a Working Notebook<\/b><\/h3>\n
From Generative Models to Production AI<\/b><\/h3>\n
How This Module Fits Into the Broader CS & AI Curriculum<\/b><\/h2>\n
A Program Built Around Intelligent Systems in Production<\/b><\/h3>\n
Generative Models Build on Earlier AI Foundations<\/b><\/h3>\n
The Curriculum Moves From Models to Production<\/b><\/h3>\n
\n<\/span><\/p>\n\n
Why This Matters for Future AI Careers<\/b><\/h2>\n
These Skills Support More Than Basic AI Tool Use<\/b><\/h3>\n
BU Connects This Preparation to Advanced Technical Roles<\/span><\/h3>\n
\n<\/span><\/p>\n\n
Start Building Generative AI Systems with an Online MS in Computer Science and AI Degree From BU<\/b><\/h2>\n