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Friday, June 21, 2024

Exploration and Challenges of LLM in To B Scenarios: From Technological Innovation to Commercial Implementation

June 21, 2024

 With the rapid development of Large Language Models (LLM) and Generative AI (GenAI) globally, their implementation in To B scenarios in China has also attracted widespread attention. Although these technologies have shown great potential in various fields such as intelligent customer service, corporate training, AI interviews, marketing acquisition, data analysis, legal consultation, and product development, their practical application is still in the exploratory and pilot stages. This article will deeply analyze the main challenges faced by LLM in commercial implementation and discuss how to combine technological innovation with industry needs to realize the commercial value of LLM.

Security Compliance: The Bottom Line of Business Operations

In any business operation, security compliance is an inviolable "red line." The commercial application of LLM must also comply with national laws and regulations to ensure information security and content compliance. Additionally, different industries have specific requirements for security compliance. For example, the financial and government sectors often require private deployment of LLM to protect data privacy and security. Therefore, enterprises using LLM must establish strict security compliance systems to ensure all operations are within the legal and industry regulatory frameworks.

Accuracy: Improving the Quality of Generated Content

The accuracy of generated content is a major challenge for LLM in To B scenarios. Issues such as inaccurate data generation, unsatisfactory document effects, and hallucinations can affect its reliability in commercial applications. To address this problem, many vendors are exploring technologies such as Function Calling and Retrieval-Augmented Generation (RAG) to enhance the real-time and accuracy of generated content. These technical methods can reduce the occurrence of "nonsense" by the model to a certain extent and enhance its credibility in practical applications.

Controllability: Ensuring Depth of Use

Security and controllability are crucial factors determining the depth of use of To B products. In addition to meeting compliance and accuracy requirements, permission management is also essential. In knowledge management scenarios, how to enable LLM to output the correct knowledge to different permissions in different scenarios is key to achieving dynamic permission management. Without effective permission management, the application scope of LLM will be limited, making it difficult to land in scenarios involving all employees and significantly reducing its business value.

Usability: Simplifying Operational Processes

Usability is another important factor for the widespread application of LLM. Some SaaS vendors have found that customers and even internal personnel do not know how to use Prompt tools. Therefore, developing Prompt tools that are user-friendly and do not require coding to build business applications is crucial. This not only improves the user experience but also accelerates the promotion and application of LLM in various enterprises.

Scalability: Achieving Universal Benefits

To achieve scalability, LLM technology must be widely used in a particular industry or customer group. However, due to cost, implementation capability, and technical stability constraints, some SaaS products combined with LLM technology can only serve a small number of high-ticket customers and have not yet achieved comprehensive promotion. To solve this problem, SaaS vendors need to collaborate with AI ecosystem vendors and customers to build standardized solutions. By combining "large models + services," they can create more universally applicable business models, thus achieving true scalability.


The implementation of LLM in To B scenarios still faces many challenges, but these challenges are also driving continuous technological progress and innovation. By continuously optimizing in terms of security compliance, accuracy, controllability, usability, and scalability, LLM is expected to bring greater value to enterprises in the future, achieving deep integration of technology and business. Enterprises should actively explore and pilot these technologies, accumulate experience in practice, and continuously improve and optimize solutions to achieve widespread application and commercial success of LLM in various industries.

TAGS

LLM for business, generative AI applications, intelligent customer service solutions, corporate training with AI, AI interview systems, marketing acquisition technology, data analysis with LLM, legal consultation AI tools, product development with generative AI, security compliance in AI applications