Demystifying AI: Enabling Everyday Expertise

A prolific business challenge of our modern era is how to integrate artificial intelligence (AI) into existing processes. Not long ago, AI was tedious to integrate and lacked flexibility. Performing document automation meant relying on key-value extraction that required defining a complex rule system of either regular expressions or positional targeting. Now, large language models (LLMs) allow complex data extractions with minimal configuration and enhanced contextual awareness.

Like the advancements in AI, we've witnessed and participated in other technological leaps. The most obvious example is in handheld computing and connectivity. Our smartphones enable us to solve complex problems in real time and communicate globally with others without needing to understand the minute details of how the smartphone operating system interacts with the hardware or how the RF transceiver sends and receives data. Instead, we form a higher-order understanding that allows us to leverage this technology with ease. 

That same process can be applied to the latest AI breakthroughs. At the lowest level, technologies like vector databases and large language models perform fixed mathematical calculations. In the case of a vector database, the process reduces content like images or text to a number, specifically a vector. The numbers are then stored in a way that facilitates finding related numbers.

For example, a financial institution could create a vector database and leverage it to identify fraudulent transactions. Each financial transaction would be converted to a vector. When a transaction is being verified, a search would be performed to identify the most similar records. Using that information, the financial institution could determine if the transaction is related to fraudulent transactions and take action.

Even more impactful, LLMs have the ability to generate content based on prompts. Like vector databases, LLMs first reduce the parts of the prompt to a number; in this instance, it is referred to as an embedding. Those embeddings are then used by the LLM to compute the next word. For example, an insurance company could utilize an LLM to improve their customer support process. The audio from a phone call between their support agent and customer would be sent to the LLM workflow. Using specialized prompts, an insurance company could gain insight into the customer's satisfaction, whether the issue was resolved, or even if the customer behaved in a suspicious or fraudulent manner.

These descriptions and examples are just the beginning. The key takeaway is that you don’t need to be a data scientist or machine learning expert to amplify your efforts and transform your workday using these technologies. What does that look like for your organization? Your deep understanding of your business's unique processes and pitfalls has already equipped you to take on this challenge. KnowledgeLake is here to provide support. The KnowledgeLake platform was designed to reduce the burden of integrating with these technologies, allowing you to unleash your expertise with agility and speed.

Ready to unlock the potential of AI in your business without the technical hassle? Contact us for a personalized demo and see how KnowledgeLake's user-friendly platform can elevate your expertise and streamline your operations.