From Unstructured Data to LLMs
According to a study done by Gartner, It's estimated that about 80% of the data within enterprises is unstructured as shown on Figure 2.1 This vast reservoir of information holds immense potential value, as it encapsulates the historical functioning and decision-making processes of a business. The challenge lies in unlocking this value by transforming this unstructured data into automated systems that can make informed decisions and recommend actions.
LLMs have emerged as a powerful tool for leveraging unstructured data to detect patterns and answer questions. These models can customize and interpret huge amounts of data, allowing businesses to build AI systems with instant access to extensive and diverse information sources. These systems are capable of formulating responses, answering questions, and identifying patterns based on historical and real-time data.
By integrating LLMs, enterprises can significantly enhance their operational efficiency, automate complex tasks, and make more informed decisions, ultimately driving innovation and competitive advantage.
Here are some applications of LLMs for unstructured data:Customize Contextual LLMs: LLMs can be tailored to understand the specific context and specifics of a business's operations, as shown in figure 2.2. This customization allows the models to deliver more relevant and accurate outputs by leveraging the unstructured knowledge base or domain-specific data of the organization.
Figure 2.2: The life cycle of a generative AI application powered by a customized foundation model attaching the domain specific data to provide context for the LLM
Processing Unstructured Data: Businesses generate and interact with vast amounts of unstructured data daily, such as emails, reports, customer reviews, and social media posts. LLMs are adept at processing this data, enabling them to summarize content, suggest productivity enhancements, or perform effective sentiment analysis.
