Some Large Language Model Use "Off the Shelf" Cases are Fine; Others Will Require Custom Models

The value of large language models (LLMs) varies significantly based on the specific business use case. Financial modeling might require a custom model built on a firm’s proprietary data, but lots of other content creation can use “off the shelf” models. 

A custom LLM trained on a firm's proprietary financial data, financial news, and reports would yield far more accurate and relevant insights for tasks such as predicting customer demand for the firm’s products, assessing its degree of financial risk or operating costs. 

Scenario	Value Provided	Examples	Use Cases	Limitations
General Purpose Language Model	Broad range of capabilities, readily available, cost-effective for basic tasks	Generating text, translating languages, writing different kinds of creative content, answering your questions in an informative way	Content creation, communication, information retrieval, basic automation	May lack domain-specific knowledge, lower accuracy for specialized tasks, potential for bias
Specific Model Adapted for Individual Firm	Tailored to specific needs, higher accuracy and performance, improved security and privacy	Analyzing financial data, predicting customer churn, optimizing marketing campaigns, personalizing customer experiences	Financial modeling, risk management, customer service, product development	Higher development and deployment costs, limited to specific tasks, requires ongoing training and maintenance

A custom LLM trained on internal customer data, surveys, and support transcripts would excel in tasks such as identifying churn risk or patterns of buying behavior. 

On the other hand, off the shelf models might be quite suitable for analyzing broader customer sentiment trends and identifying emerging topics of interest for the range of company products. 

The same is likely true for general content creation such as code generation and scripts, marketing copy or website content, as well as language translation.

So the optimal approach might involve a hybrid strategy, leveraging both custom and general models.