Generative AI, a type of AI that can create new content and ideas including conversations, stories, images, videos and music, has taken the world by storm. Like all AI, generative AI is driven by ML models—very large models that are pre-trained on large amounts of data and are commonly referred to as foundation models (FMs). And consumer-facing applications like ChatGPT have demonstrated how powerful modern machine learning models have become.
Organizations can apply generative AI across all lines of business, including engineering, marketing, customer service, finance and sales, to transform nearly every aspect of how they operate. They are developing virtual assistants and new call center features to enhance the customer experience, increasing employee productivity through conversational search and code creation, and improving operations with document processing and data-rich cybersecurity. These changes are happening in industries from healthcare to financial services as companies use generative AI to drive better results faster.
This doesn’t mean that only generative AI will transform your business. To fully realize the benefits of generative AI, you need to differentiate the applications you build with it, which requires a deeper dive. Generative AI relies on data not only to create content, but also to learn and evolve. Every great generative AI application is backed by a solid data strategy that helps you customize your models and create a competitive advantage.
If you want to build GenAI applications that are unique to your business needs, your organization’s data will be the differentiator that delivers.
Check your data foundation
Generative AI, with its ability to create content, relies on data. In a general context, the better the quality and relevance of the input data, the more refined and applicable the outputs will be. Data doesn’t just feed AI; It shapes it, providing a foundation on which AI learns and evolves.
There are several ways that organizations can use data in their generative AI applications. While some companies will build and train their own large language models (LLMs) with large amounts of data, others use them to fine-tune existing foundation models for their unique business needs or add context to prompts through retrieval augmented generation. Will use organizational data. RAG), a framework for feeding LLM with accurate, up-to-date information from external sources to improve LLM response.
For example, if you’re an online travel agency that wants to create personalized travel itineraries, you’ll want to use the customer profile data in your database to make recommendations based on past trips, web history, and travel preferences. You can then marry that data with other data sources such as flight and hotel inventory, promotions and similar travel details.
The key to making all of these use cases work is quality data. Actually, according to Amazon Web Services CDO SurveyThe number one challenge for organizations in realizing the potential of generative AI is data quality.
“The only thing most large service companies can do is build a generative AI strategy,” said Archana Vemulapalli, head of product and global strategy for data and AI at AWS. “You need to have one data. data and AI strategy.” Vemulapally recommends creating a data strategy that starts with data collection and ends with data management, ensuring your data is accessible, reliable and secure at every step.
First, it’s essential to base your data strategy with a scalable infrastructure for data storage. Generative AI relies on large amounts of data, including text, images and video, so your infrastructure needs to be able to handle the volume, variety and velocity of the data you collect. This includes breaking siled data and consolidating data controlled by different groups across your organization. You’ll also want to choose data storage that’s optimized for your specific use case. For example, generative AI applications often use vector data, so you’ll need a data store capable of searching and storing this type of data.
Next, the quality of the data used to train generative AI models significantly affects their performance, as the models learn from the data they are trained on. Along these lines, it’s important to make sure your data is representative of your dataset and that you’ve taken steps with that dataset to identify and mitigate biases.
It’s also important to have tools to easily connect your various data sources These tools may include data integration platforms, APIs, and software connectors—
For example- a- Service (SaaS) applications, on-premises data stores and other clouds.
Finally, you need to ensure that your manufacturers have easy but controlled access to data. Establishing data governance practices is critical to promoting the integrity, security and compliance of your data It includes defining data standards, access control, data pipelines and data lifecycle management. This includes implementation of safeguards to protect sensitive data and consideration of relevant data protection regulations. Data governance promotes the use of data that is reliable, traceable and compliant with privacy and legal requirements.
Once you have a solid end-to-end data foundation, you’re ready to innovate with generative AI.
Start with a small but powerful problem
When creating a plan for using generative AI, start by focusing on the business goals. “Think about what levers you want to exercise with generative AI,” says Vemulapally. “Is the goal to drive customer experience, find new revenue streams, or create a new product and see how it scales? Align your strategy.”
The next step is to find a use case that can quickly show meaningful impact. “What time-consuming, difficult or impossible problems can generative AI help solve? Where do you have the data to help in this process?” Vemulapally said. “Think big about opportunities, but start small. Start with a problem that causes daily frustration—one that your organization will see real value in fixing.”
“Just pick a known pain point and solve for it. Don’t wait for a silver bullet use case. Your use case will evolve,” she says. “Just testing, just going.” And once your use case is identified, you can work backwards to identify the relevant data needed.
Choose and customize a foundation model
Generative AI is driven by ML models—very large models that are pre-trained on large amounts of data and are commonly known as foundation models (FMs). FMs learn to apply their knowledge in a wide range of contexts through pre-training exposure to all the different forms and myriad patterns of Internet-scale data, and these “general FMs” can be used in some use cases. But many organizations are looking for FMs that can be customized to perform domain-specific functions unique to the organization. In this case, the FM must be “fine-tuned” with the organization’s proprietary data.
AWS built Amazon Bedrock for exactly this purpose. With Amazon Bedrock’s extensive capabilities, you can easily experiment with different top FMs, customize them personally with your data using techniques such as fine-tuning and Recovery Augmented Generation (RAG), and perform complex business tasks. Managed agents can create—from booking travel and processing insurance claims to creating ad campaigns and managing inventory—all without writing any code.
Imagine a content marketing manager who works with a leading fashion retailer and needs to create new, targeted advertising and promotional copy for an upcoming new handbag. To do this, they provide some labeled examples of their best performing taglines from previous campaigns, along with corresponding product descriptions. Bedrock creates a separate copy of the base foundational model that is accessible only to the customer and trains this personal copy of the model which will then automatically start generating effective social media, display ads and web copy for new handbags. Now, marketing managers have a new ad campaign informed by their historical data, without having to invest in a new model or incremental training, keeping the organization’s data private and secure.
For an organization that chooses FM, it is important to keep data private and secure and to maintain control over who can access the models. “You want to make sure your organization has the right rails in place to protect data and IP,” says Vemulapalli. “Your data is your differentiator and your ultimate competitive advantage.”
Train and develop responsibly
New challenges in managing data responsibly arise from the sheer size of generative AI’s open-ended foundation models trained by billions of parameters and raise new issues in defining, measuring, and mitigating responsible AI concerns throughout the development cycle. Accuracy, fairness, intellectual property considerations, toxicity, and privacy must all be considered on a new level.
“Consider your position with AI on responsible AI, transparency, data collection, security and privacy,” Vemulapally said. “How can you ensure that technology is used correctly, fairly and appropriately?” Organizations should train on these considerations, build them into governance and compliance frameworks, and factor them into vendor selection processes to select partners who share the same values.
“Everyone is committed to being responsible,” Vemulapally said. “What matters is how it is executed and enforced.”
There is also the issue of training and developing your people. Consider the technical skills required to use these new technologies and how to incorporate them into your organization. You can focus on building technical skills as well as skills like critical thinking and problem solving. We ultimately want people assisted by AI to solve real business challenges and to critically evaluate and question inferences from ML models. This is especially important with generative AI models that distill data rather than providing considered answers.
Be prepared for the next thing
Establishing an end-to-end data foundation is essential to a successful generative AI strategy, and treating your data as your greatest asset will guide your steps on that journey. That solid data foundation, in turn, will set you up for rapid innovation. “Customers are being thoughtful and fast,” says Vemulapally. “We are currently in a period of intense testing and rapidly transitioning to an at-scale implementation. Everyone recognizes the need to move quickly.”
Learn more about innovating with Generative AI AWS for data.