AI seems to be a virus that continues to spread and cannot be stopped, the growth of its innovation has a revolutionary impact. Talking about AI certainly cannot be separated from data, it is because of this data that AI becomes smarter in thinking. Data can be said to be of high quality if the data has high accuracy, is relevant, timely and complete, all of which are needed to help the data processing process, better known as the Large Language Model. Accurate data is an important attribute for large language models (LLM) to build their understanding of language and context. By ensuring that training and decision-making processes are based on reliable and relevant data, the value of the results produced by the LLM increases significantly. Quality data serves as a reliable reference point for LLMs, enabling them to produce accurate and insightful output.
According to Genie Yuan, Regional Vice President APAC and Japan, Couchbase, data timeliness, in particular, plays a critical role in determining whether an LLM is providing inaccurate or outdated output, especially in dynamic environments and industries where information is constantly evolving, such as finance , healthcare, and technology. Through the utilization of real-time and continuously updated data, the LLM remains current and relevant.
“What makes data timeliness even more important is the challenges associated with utilizing real-time data. For example, the write-back latency gap that exists in most analytical systems prevents the analytical system from writing back acquired values into the operational system. As a result, delays in taking action based on these insights can be substantial, ranging from minutes to days. These differences hinder the ability to speed up action taking and limit the real-time aspects of analysis, in contrast to the seamless nature of database transactions,” he said.
Genie explains in detail, there are five ways organizations can overcome discrepancies and calculation errors generated by LLM due to poor quality data input: data quality control, continuous monitoring, model adjustment, retrieval-augmented generation (RAG), and human oversight. Poor quality data input will produce poor output.
To ensure that data is accurate, relevant, timely, and complete, organizations must implement real-time data quality control measures, identify data discrepancies, and correct them before using them to train LLMs. On the other side of the overall process, there should be ongoing monitoring of the output produced by the LLM to identify patterns of inaccuracy. Real-time data can be used for ongoing evaluation, allowing organizations to detect and address discrepancies immediately.
“So the LLM itself, and not just the data, can be adjusted to reduce differences. By providing domain-specific real-time data, the quality of the model output can be improved. However, this requires continuous data re-updating which is time consuming and also expensive,” explained Genie.
Alternatively, LLM responses can be enhanced by basing them on accurate and up-to-date information from external knowledge repositories. By including facts from reliable sources, organizations can increase the accuracy and reliability of the output the LLM produces. Lastly, according to Genie, human supervision and intervention, in the form of trained experts, can provide the possibility of LLM results being reviewed and verified.
Genie added that, although this is only the tip of the iceberg of what an LLM can do, the ability to produce text, video, audio and images based on user requests has had a dramatic impact on business operations. For example, marketing teams can save time and money developing content such as personalized emails by using ChatGPT.
Not only that, but Genie also mentioned that marketing analysis and business activities can also use LLM with the support of strong data with sharp analysis results and is able to help marketing activities.
“Other possible uses of LLM in marketing include chatbots that can better understand customer questions and provide relevant responses in natural language, real-time generation of product suggestions for customers based on their browser activity, quality assurance of customer service representatives based on previous conversations with customers, and chatbots which provides easy access to the marketing team’s database,” he explained.
It’s not just marketing that Genie also mentions, that outside of marketing, the LLM’s ability to sift through large amounts of text to create responses is also useful in helping software developers. Rather than manually writing or even copying lines of code from a library, developers can simply provide a few instructions to LLM-supported tools.
His concerns about ChatGPT and the LLM in general are understandable. Many organizations also strictly prohibit the use of AI and its adoption into their work systems. Genie understands this, because every new technological development raises many questions. But instead of outright banning the use of LLM-based solutions, concerns about them can be addressed through human oversight. By carefully studying the technology’s pros and cons and developing usage guidelines, decision makers can address and mitigate potential risks.
As AI continues to advance, future developments or innovations that we can expect to see in the field of AI-based conversation is one of the key areas of improvement that will be made in contextual understanding as AI models become more adept at deciphering conversational context. This will result in more coherent and relevant interactions, allowing AI models and tools to better understand the nuances of conversations and provide accurate and timely responses.
Another important innovation is the integration of multiple modalities, including text, speech, and visual input. This will allow for more natural and dynamic conversations, as AI will be able to process and respond to different types of input smoothly. The ability to understand and respond to visual cues, for example, will greatly improve the user experience and make interactions more engaging and intuitive.
In addition to improving contextual understanding and multi-modal conversations, Genie also mentioned that AI-based models will also develop better emotional intelligence. This means they will be able to recognize and respond to user emotions, adding a human-like element to interactions. By understanding a user’s emotional state, AI will be able to tailor responses and provide appropriate support, creating more personalized and empathetic conversations.
“Ethical considerations will also come into play as conversational AI continues to evolve. Businesses must ensure responsible and respectful interactions with users. This includes addressing bias, protecting user privacy, and encouraging diversity and inclusivity. AI models will be designed to comply with ethical standards, creating a safe and inclusive space for conversations between businesses and their customers,” concluded Genie.
Real-time data integration will play an important role in these future developments. By staying current and context aware, AI models will be able to provide more meaningful and effective conversations. This requires integrating data from multiple sources, such as customer databases, social media, and real-time feedback. By having access to a wealth of data, AI models will be able to produce more accurate responses and recommendations, resulting in increased customer satisfaction and better business outcomes.
