Introduction:
In today's fiercely competitive business landscape, harnessing proprietary and specialized knowledge using natural language processing has become a key strategy. Large Language Models (LLMs), such as OpenAI's GPT-4, allow organizations to create an AI Knowledge Asset to learn from their unique text-based data sources, including Slack channels, emails, and more.
In this blog, we'll explore the potential of this technology and provide insights into implementing such a solution. For the sake of simplicity, we'll focus on the AI learning of text data only, leaving image, voice, and video formats for future posts. This article is intended for business professionals; an engineering explanation would require a more detailed technical breakdown. If you're interested in a deeper dive into the engineering side of things, feel free to send comments to me at dan@claritee.ai. I would be more than happy to draft a developer-centric post.
Understanding LLMs:
LLMs are advanced computer programs that emulate human interaction with language, mirroring how people read, write, and communicate. These models leverage natural language understanding (NLU) and natural language generation (NLG) capabilities to process and produce text-like human communication. Trained on copious amounts of internet text, LLMs pick up grammar, vocabulary, and even some generalized knowledge. Their adaptability allows them to perform various tasks, from answering questions and summarizing a text to translating languages and creating original content. By harnessing NLU and NLG, LLMs can offer valuable insights and contribute effectively to various industry applications.
OpenAI's GPT-4, an advanced multi-modal Large Language Model, excels at processing text, demonstrating its adaptability and potential for diverse business applications. As the core technology behind the famous ChatGPT, GPT-4 has attracted strategic backing from Microsoft, propelling OpenAI to dominate the AI industry. Nevertheless, the competitive landscape is constantly changing, with new LLMs entering the market regularly, courtesy of both open-source collaborations and commercial enterprises looking to capitalize on the growing demand for AI-powered solutions. My expertise lies with the OpenAI model APIs for ADA, GPT-4, and ChatGPT.
Creating a Proprietary AI-enabled Knowledge Base:
The AI-enabled Knowledge Base is a framework for readying an organization's data for AI learning. There are five key areas to consider when setting up a company-specific AI.
Knowledge Base:
Data Collection & Preparation: Start by identifying and collecting your organization's unique data, including structured and unstructured information. Ensure the data is AI-friendly by breaking long paragraphs into shorter sentences, grouping related pieces of data, and converting tables in PDF files into AI-readable formats such as CSV or JSON. This process, also known as "chunking" the data, ensures the AI model can effectively learn from your organization's specific knowledge, helping you unlock its full potential. Chunking makes information more digestible for the AI model, leading to improved learning outcomes.
Data Training: Train the LLM using your organization's data. This enables the model to learn and understand the nuances of your business and industry. This step involves embedding the data in a vector database, which stores data in a format that simplifies data retrieval via natural language. Pinecone is an example of such a vector database. One can use OpenAI's ADA embedding model to learn text data and convert it into vectors. We've successfully executed this step, which is not as complex as it may sound. Any database developer can quickly learn this technology.
Data Interaction: Deploy the LLM within your organization as a chatbot, which allows users to interact with the AI Knowledge Base using natural language. This user-friendly approach helps garner feedback from the user community and provides an understanding of the types of questions the users are asking. The chatbot facilitates AI accessibility, enhancing the overall user experience.
Data Feedback: Continually monitor the LLM's performance and collect user feedback. This information helps refine and enhance the AI Knowledge Base, ensuring it stays relevant and valuable for your organization. As updates and improvements are made, the accuracy of the AI model should gradually increase. We've chosen to store the feedback in the Snowflake cloud data warehouse application.
Data Security: Data privacy and security are vital considerations when establishing a company-specific AI Knowledge Base. Be cognizant of potential vulnerabilities in the vector database and OpenAI's language models. For instance, Pinecone operates on a secure, fully managed AWS infrastructure. Customer data is stored in isolated containers and encrypted at rest and in transit. OpenAI reserves the right to review the data for model improvements but limits access to user data as stated in its privacy policy. Stay updated with the latest data privacy regulations and best practices to ensure compliance and protect your organization's sensitive information.
Industry-Specific Application Prototypes:
Currently, we are developing prototypes that support clinical and revenue cycle co-pilot applications for medical providers and equipment servicing for manufacturers. These prototypes aim to showcase the versatility and adaptability of LLMs across various industries.
For medical providers, we anticipate that the AI Knowledge Base will assist with patient data retrieval by physicians and other clinical staff, enhancing overall care and communication. The revenue cycle management process could be optimized for payment accuracy and denial reasons by enabling AI models to learn from payer contracts and actual claims. By learning from clinical notes (including test results and prescription data), claim lines, and payer contracts, the LLM can assist providers in identifying patterns and trends, automating tedious tasks, and improving collections and patient communications.
For manufacturers, the AI Knowledge Base can learn from equipment data, maintenance records, documentation, and industry-specific knowledge. We're developing a Copilot application to offer insights into equipment performance and cross-platform technical & functional documentation, thereby enhancing field service calls, training, and customer support.
Embracing AI Incrementally:
AI is a dynamic field, continually evolving with new advancements and shifts. Organizations need to adopt a gradual, phased approach to implementing AI solutions. Starting slow and incrementally adding capabilities as results materialize can ensure a smooth transition and effectively harness the potential of AI. Continuous learning and adaptation are crucial for developing and fine-tuning AI applications. Consider setting up an AI innovation lab to foster experimentation.
Conclusion:
Establishing and implementing a proprietary AI Knowledge Base using Large Language Models, like OpenAI's GPT-4, can transform how organizations access and utilize their specialized knowledge. Focusing on critical areas such as data collection and preparation, data training, data interaction, data feedback, and data security can help businesses construct a robust AI-driven solution that fully leverages their data.
Organizations should adopt an incremental approach to implementing these solutions as AI progresses. Starting slow and gradually expanding capabilities as results can help companies effectively integrate AI into their operations, gaining a competitive edge in their respective industries.
Whether you're in healthcare, manufacturing, or any other sector, now is the time to explore the possibilities of AI and create a knowledge asset that will propel your organization forward in today's rapidly evolving business environment. If you have any questions or need a sounding board to discuss the approach outlined, please get in touch with me at dan@claritee.ai.