The value of Large Language Models for enterprise Part.3
Featuring "The Value of Large-Scale Language Models in the Enterprise” which is a four-part series. Part.3 is a continuation of “How I can use Large Language models in my business?”, with “3. Automation for everyone”, “4. Processing unstructured data” and “5. Customer Support”
How I can use Large Language models in my business?
Part.3 is a continuation of Part.2, we have put together a summary of the various applications of LLMs in the enterprise context. This overview will give you a sense of the types of problems that LLMs can help you solve, and the ways in which they can be integrated into your existing workflows.
3. Automation for everyone
Large language models have the potential to make some APIs obsolete by allowing people with no knowledge of code to automate common actions and connect different software tools without writing code. APIs, or application programming interfaces, are a set of protocols and tools for building software applications. They enable different software tools to interact with each other, making it easier for developers to create new applications and services.
However, APIs can be complex to work with, requiring developers to have a deep understanding of coding languages and syntax. This can create a barrier for people who want to automate common actions or connect different software tools, but don't have the technical skills to do so.
Large language models can help to overcome this barrier by providing a more intuitive interface for automating actions and connecting software tools. By using natural language commands, users can instruct the model to perform specific actions, such as sending an email, scheduling a meeting, or updating a database. The model can then take care of the technical details, such as interacting with APIs, so that users don't have to worry about the underlying code.
For example, imagine a marketing team wants to automate their social media postings. Instead of writing code to interact with different social media APIs, they could use a large language model to automate the process. They could provide the model with a natural language command, such as "Schedule a tweet for 2pm tomorrow with this image and caption." The model could then take care of the technical details, such as interacting with the Twitter API, to schedule the tweet.
By making it easier for non-technical users to automate actions and connect software tools, large language models have the potential to democratize the automation process. This could lead to a more efficient and productive workforce, as more people can take advantage of automation tools without needing advanced technical skills.
4. Knowledge management and business intelligence
Knowledge management and business intelligence are two areas where large language models (LLMs) can have a significant impact on enterprise operations. By using LLMs to analyze and summarize large amounts of data and content, companies can improve decision-making, streamline workflows, and drive business growth.
Automatically generated summaries of documents and other content make it easier for employees to quickly access the information they need. For example, a law firm can use a large language model to summarize legal briefs or case law, making it easier for attorneys to quickly find the information they need to support their cases.
Search and synthesis systems powered by large language models can be a game-changer for companies when it comes to accelerating internal work. By providing easy and efficient access to a company's internal documents, these systems can help any worker quickly find the information they need to do their job effectively. For example, imagine a marketing professional who needs to create a new campaign for a product. Instead of spending hours manually searching through past campaigns and marketing materials, they could use a search and synthesis system powered by a large language model to quickly find relevant information and even generate new content.
One industry where search and synthesis systems have proven especially useful is healthcare. Healthcare providers deal with an enormous amount of data and documentation, from patient charts and medical histories to research studies and clinical trials. By using a search and synthesis system powered by a large language model, healthcare providers can quickly access this information and use it to inform patient care and research efforts. For example, a physician who needs to diagnose a rare disease could use the system to search for similar cases and relevant medical literature, saving valuable time and potentially improving patient outcomes.
Another industry where search and synthesis systems have proven useful is industrial manufacturing. Manufacturing companies deal with large volumes of technical documentation, such as schematics, assembly instructions, and safety guidelines. By using a search and synthesis system powered by a large language model, workers can quickly find the information they need to build and maintain products, troubleshoot issues, and ensure safety compliance. For example, a worker who needs to repair a machine could use the system to quickly find the relevant schematics and troubleshooting guides.
Finally, the retail sector is another industry that can benefit from search and synthesis systems powered by large language models. Retail companies deal with large volumes of customer feedback, sales data, and product information. By using a search and synthesis system, workers can quickly find the information they need to make informed decisions about product development, sales strategies, and customer service. For example, a customer service representative could use the system to quickly access customer feedback and complaints, enabling them to provide a better customer experience.
5. Customer Support
Large language models can be a valuable tool for companies looking to improve their customer service operations. Chatbots and virtual assistants powered by large language models can provide immediate assistance to customers, reducing the need for human support agents to handle routine inquiries. These chatbots can be trained to recognize common queries and provide relevant answers quickly and efficiently, resulting in higher customer satisfaction rates and lower support costs for the company.
For example, a retailer can use a chatbot to help customers track their orders, check product availability, and process returns or exchanges. By training the chatbot to recognize common queries and provide accurate information, the retailer can reduce the workload for human support agents, freeing them up to handle more complex issues.
One advantage of large language models is their ability to connect with a company's internal documentation, allowing them to provide up-to-date and accurate information to customer queries. With the correct access scopes, chatbots and virtual assistants can quickly search through a company's internal knowledge base and provide the customer with the information they need. This can significantly reduce the amount of manual work required to respond to customer inquiries, freeing up human support agents to handle more complex issues.
In addition to improving customer service, chatbots and virtual assistants can also be used for internal communication and collaboration. For example, a company can use a virtual assistant to schedule meetings, set reminders, and send notifications to team members. By automating these routine tasks, the virtual assistant can help increase productivity and free up employees to focus on more important work.
Continue to Part.4
Co-founder and CEO
Tiago holds a Master's degree in Theoretical/Mathematical Physics and a PhD in Biophysics from Ludwig-Maximilians University Munich. After graduation, he joined Google DeepMind as a research engineer. There he worked on a number cutting-edge research projects which led to publications in international machine learning conferences and scientific journals such as Nature. He then joined Cogent Labs, a multinational Tokyo based AI start-up, as a lead research scientist. In August 2020 co-founded Recursive Inc, and is currently CEO.