Generative AI Agents' Identities & Characters: The Business Case for LLMs Fine-Tuning

In the 56th edition of?this newsletter, entitled “GenAI-Powered Intelligent Agents as Collaborative Workforces: The Power of Fine-Tuned LLMs,” it was concluded that instead of telling the?“General-Purpose Large Language Models”?to switch between different roles many times, which may represent a waste of time and resources as well as be considered as the opposite of the previously explained?“Attention-Tuning Strategies,”?a better strategy would be to use several?“Fine-Tuned Large Language Models”?to automate or augment several tasks within the complex daily workflows. This means that several?“Generative AI-Powered Intelligent Agents”?that are powered by different fine-tuned large language models and work as a?“Collaborative Workforce”?should be used to fully automate or augment complex workflows related to specific jobs. This will ultimately increase efficiency and productivity in the workplace.

Now, a legitimate question may be raised. Why, in the first place, does the end user of Generative AI models have to choose between explaining the “Performed Role” in detail to the general-purpose large language model or using a “Role-Based Fine-Tunined Large Lanague Model?”?The focus of this brief article will be to answer this question.?

The answer to this question is so intuitive. Each job role has its own?“Identity and Character,” which appears evidently in the?“Role-Specific Style and Tone.”?For example, the lawyer may use a serious style in his communication as well as a formal tone in his produced documents and delivered speeches. On the other hand, a technology-savvy enterpriser usually uses a casual style in his communication with a more relaxed and informal tone. Both cases seem logical and suitable for their respective professions.

Of course, at the end of the day, some intuitive metrics can be used as a quick guide for decision-making to speed up the process and ensure the best possible outcome. The most intuitive metrics are?“Return on Investment - ROI”?and?“Time to Market.”?Both metrics are intuitive and can even be used qualitatively.

Hence, and to conclude this brief edition of the newsletter, despite the associated costs and technical complexities required for retaining the?“General-Purpose Large Language Models”?to modify it into a?“Role-Based Fine-Tunined Large Lanague Model,”?significantly distinguished job roles with significantly distinguished identity and character may force the decision maker to select the?“Fine-Tuning Strategic Option”?over its alternatives such as the will know?“Retraival Augmented generation - RAG Strategic Option.”?In this case, the?“Role-Based Fine-Tunined Large Lanague Model”?will be able to show significantly distinguished?“Role-Specific Style and Tone”?that convey significantly distinguished?“Job Specific Identities and Characters”?performed by the?“Collaborative Generative AI Agents Workforce.”?“Return on Investment - ROI”?and?“Time to Market”?are typical intuitive metrics to select the best-fit strategic option.