With the rapid development of artificial intelligence (AI) and large language models (LLMs), companies are rushing to incorporate automated technology into their networks and applications. However, as the age of automation persists, organizations must reassess the data on which their automated platforms are being trained.

To maximize the potential of AI using sensitive data, we must first address why the data being input into AI and LLM platforms matters.

Platforms like ChatGPT operate based on a massive dataset of text and codes from various online and offline sources. Producing inaccurate AI-generated data can be as simple as inputting outdated or unsuited data for constructing the LLM model. As inaccurate data enters the continuous loop of repackaged information, the cycle of inaccuracy is only exacerbated within AI systems.  

Organizations leveraging open-source LLMs like ChatGPT and Copilot must realize that these models are trained on massive amounts of data from various unchecked sources. The data ingested by these models has the potential not only to hallucinate but also to produce biased outcomes. There is no way to know where the training data has come from, so you don’t know what quality of output to expect from the model. How accurate is the data? Is the AI basing its results on undesirable information? 

Instead of relying on generalized data that may tell you next to nothing of true business value, we should instead focus our efforts on training LLMs on high-quality, sensitive data that organizations can feel confident about. 

The Importance of Maintaining High-Quality Data 

It’s simple: If we train models based on poor-quality data, we will receive poor-quality results. To produce results tailored to their customers’ needs, organizations must prioritize the quality and accuracy of their input data to achieve high-quality business outcomes with AI and LLMs. This ensures more reliable and accurately generated insights and more personalized and detailed customer experience, leading to sustainable and successful business operations.

To maintain accurate data as the world accelerates into the age of AI, we must collectively commit to continuously improving four focus areas.

• Understanding Data: It is essential to understand and review data carefully to know how it interacts with AI. This can be achieved through data profiling and regularly conducting audits to assess the integrity and reliability of your data.
• Automation: Improving automation involves closely integrating LLMs with existing application development processes. Doing so eliminates the need to make specialized copies of the data and reduces redundancy and inaccuracies.  
• Data Classification: We need better data classification so models can help structure the data to visualize the relationships, trends, and other factors influencing decision-making. Teams can incorporate classification into the model itself, and some cloud security providers can deliver.
• Human Oversight: As advanced as they may seem, it’s important to remember that AI and LLM capabilities are only as comprehensive as the information humans input. As automated technology evolves, top-notch input data is vital to avoid AI-generated outcomes. 

Leveraging these methods enables organizations to harness the untapped potential of sensitive data while remaining secure and compliant with privacy regulations. 

Achieving High-Quality Outcomes

Let’s take hotel loyalty programs as a prime example of the power of using sensitive data while training LLMs. By utilizing AI to analyze customer data, hotels can offer personalized recommendations based on past preferences and behaviors. Incorporating AI technology can also allow leadership teams to anticipate customer needs and proactively address them. By providing a personalized and seamless experience, businesses using well-trained AI technology increase customer satisfaction and loyalty, enhancing the overall customer experience and encouraging clientele to continue participating in the loyalty program. 

Maintaining accurate data also has countless positive outcomes beyond enhanced customer experience, including improved security posture, streamlined decision-making, increased efficiency, and valuable business outcomes. It is evident that the accuracy and reliability of input data are directly correlated with the outcomes generated by automated technologies. However, for AI and data quality control to exist harmoniously, solving data quality issues requires a multifaceted approach that accounts for the continuous evolution of today’s automation.

Using high-quality, sensitive data is crucial for organizations looking to maximize the potential and efficiency of AI and LLM technologies. However, many emerging automated technologies cannot fully guarantee these capabilities due to their early stage of development. To achieve high-quality business outcomes, organizations must prioritize the quality and security of their data and implement a multifaceted approach to data quality control that accounts for the continuous evolution of automation. For this reason, businesses that want to leverage and rely on AI beyond the general chatbot capabilities need a way to make their most valuable and sensitive data available for model building and training. This will advance automated capabilities in the long run, providing a firm foundation for AI to produce high-quality data.

By leveraging cutting-edge technology and accurately training AI and LLM platforms, organizations can unlock transformative insights, drive innovation, and deliver enhanced customer services and experiences.