We’re currently doing a lot of work with organizations on Generative AI (GenAI) products and services. Many are building foundational data architecture and data engineering capabilities to prepare for GenAI. Others are ready to create, test, and deploy GenAI applications on the cloud. We’re seeing a wide range of skills and interest in the technology and expect to for a long time.

Fortunately, as a long-time AWS Premier Tier Services Partner with extensive AI/ML experience, we’re prepared for all types of GenAI engagements. And through our work, we’ve noticed we get asked a very similar set of questions about GenAI. In this blog post, we answer the most frequently asked questions. And we point you towards resources to go deeper with GenAI when the timing is right.

What is Generative AI?

Generative AI, or GenAI, refers to artificial intelligence technology that is capable of producing content based on prompting from a human user. GenAI can output, or “generate,” many forms of content, such as text, images, software code, and even videos with relatively simple requests. The use cases for GenAI are vast, the technology is impressive, and there is a clear market readiness. This is a big reason why GenAI adoption has skyrocketed since ChatGPT’s release in November 2022.

How Does Generative AI Work?

Generative AI is a form of AI/ML technology that aims to make accurate predictions about what users want and then provide new content accordingly. AI engineers create Generative AI tools with extensive machine learning model training and massive data sets. A large volume of data is necessary so underlying AI/ML models can apply advanced math and statistics across a vast plane of information. This is what leads to better outputs and predictions.

How Are Organizations Using Generative AI Today?

Organizations are leveraging GenAI across many enterprise functions. One of the most common use cases for GenAI is content writing. Many organizations are also augmenting their customer support function with GenAI which can handle basic interactions with customers. Furthermore, GenAI can create reports, offer tailored recommendations, process documents automatically, and summarize information. In other words, GenAI can increase employee productivity dramatically and speed up processes that would otherwise require much more manual effort.

How are Generative AI and Large Language Models (LLMs) Related?

Large Language Models (LLMs) represent a specific application of Generative AI technology. LLMs are the foundation of natural language processing (NLP) capabilities. They make it possible for humans to interact with software in a way that reflects…well, natural language. ChatGPT put LLMs on the map. Now organizations everywhere are using foundational LLMs to finetune their own text-based GenAI use cases.

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Is It Better to Use an Existing GenAI Tool or Build By My Own?

The choice between using an existing GenAI application or building one in-house depends on the needs of the business. There are many sophisticated GenAI applications on the market already, as well as foundational models that companies can build on. For most teams, it makes sense to use an existing GenAI application or start from a foundational model rather than train one up from scratch. Training AI/ML for GenerativeAI requires a tremendous amount of time, data, and expertise.

What are Hallucinations?

In the context of GenAI, the hallucination problem refers to the tendency of GenAI to return false information as if it were objectively true. Remember, GenAI is simply making statistical predictions about what should come next given what has come before. It’s not thinking critically about whether something is right or wrong in a given context. Hallucinations represent outputs that are “made up” by GenAI and have the potential to mislead users if they are not careful.

How Do I Keep My Data and IP Safe When Using Generative AI Solutions?

When working with popular third-party GenAI solutions, it’s important to remember that user requests often travel across the public internet. That means information like API keys, passwords, and corporate IP is exposed and potentially accessible to bad actors sitting between your local computer and the GenAI software on the other side of the request. Therefore, a best practice when working with GenAI is to never send sensitive data unless working with an application that explicitly uses VPNs or other private connections to transit data between users and AI/ML models.

To go deeper into this question, we wrote a separate blog post about the key security considerations organizations should keep in mind when working with GenAI.

Get Started with Generative AI Today with ClearScale

At ClearScale, we’ve developed a dedicated GenAI service – GenAI AppLink – for those who want to get started with GenAI technology but don’t know where to start. Through this service, we set organizations up with all necessary resources on the AWS cloud, install libraries, and then create a GenAI MVP with sample requests and responses. Clients can then take this application and expand on it, thanks to our thorough documentation.

This is a great way to get your feet wet with GenAI if you already have the right data architecture and data engineering capabilities in place. Visit this page to learn more about our GenAI AppLink service.

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