Fine-Tuning LLMs: How to Customize AI for Your Business Needs
By Carlos Oliveira, PhD
We have recently witnessed how Large Language Models (LLMs) like GPT-4, Claude, DeepSeek and Llama are powerful out of the box. However, their full potential may have only be uncovered after the process of fine-tuning.
The magic of LLMs frequently comes out only when you fine-tune them for specific tasks. Examples of such tasks include building a customer support bot, creating a legal research assistant, or deploying a creative writing tool.
In my profession career, I have seen examples demonstrating that the process of fine-tuning can make an LLM far more accurate. They can also make LLMs more useful for your needs as well as for your clients.
Fine-Tuning: What is the Process?
Fine-tuning is the process of taking a pre-trained LLM and further training it on a specialized dataset to adapt it to a particular domain or task.
Unlike prompt engineering (which relies on clever instructions), fine-tuning modifies the model’s weights, making it inherently better at the desired function.
Reasons to Fine-Tune an LLM
There are several reasons why businesses may want to fine-tune an LLM for their own needs. Here are some of the most important:
Better accuracy is the first reason, as fine-tuning helps in adapting and improving LLMs to a particular application. The goal is to tailor responses to a specific industry, including particularities such as the area’s jargon, specific requirements and personal needs of the industry.
Another reason for LLM fine-tuning is to achieve more efficiency, which reducing the need for further prompts. By employing specific fine-tuning measures, we can effectively reduce reliance on long and complex instructions.
Making LLM cost-effective in the long run: this is another reason that makes fine-tuning a good option. If we can make fewer API calls, this results in cheaper and faster responses.
Finaly, an important reason for LLM fine-tuning is to achieve domain specialization. This is a process that may create LLMs ideally suited to areas such as medicine, law, finance, or other niche applications.
Examples
Here are three examples of well publicized LLM fine-tuning and their results:
1. BioMedLM (Fine-Tuned for Biomedical Text)
Purpose: Specialization in medical and life sciences literature. The method used was a supervised fine-tuning on biomedical datasets (e.g., PubMed articles).
Results: Improved accuracy in generating medical summaries and answering domain-specific questions.
2. LegalBERT (Fine-Tuned for Legal Documents)
Purpose: Enhancing performance in legal text analysis, contract review, and case law summarization. The method used here was a task-specific fine-tuning performed on legal corpora (e.g., court rulings, contracts).
Results: Better comprehension of legal jargon and improved performance in tasks like legal entity recognition.
3. BloombergGPT (Finance-Specific LLM)
Purpose: Financial analysis, earnings reports, and market sentiment tracking. This GPT was fine-tuned on proprietary financial datasets.
Results: Excels in financial NLP tasks, such as earnings call summarization.
In Summary
Fine-tuning is a process that brings to real life much of the promised potential of LLMs. It also lets us build AI systems that speaks more closely our own language.
The LLM fine-tuning process may be a great option if you’re a developer, but also a business owner or researcher. Mastering this technique in your business or scientific applications can give you a competitive edge in an already complex AI environment.
I will cover in-depth aspects of LLM fine-tuning in future issues of this newsletter. Subscribe and make sure you don’t miss it.
Author Bio
Carlos Oliveira is a researcher and consultant in the areas of AI, LLM, and OR. With a PhD is Operations Research and a MSc in AI, he has championed many of the most powerful technologies that are used in the AI field such as Machine Learning, and LLM.
Carlos Oliveira worked for many SP500 companies such as Amazon, AT&T, Bloomberg, Netjets and other large businesses in areas such as E-Commerce, Transportation, and Finance. He is the author of several books in the fields of AI, programming and optimization.
Want to work with me?
There are several ways we may work together.
1. Subscribe to a paid membership and receive full business cases, and in-depth analysis of LLM technology and applications.
2. Buy full courses. I have created online courses in LLM and related topics and am in the process of creating a full LLM curriculum. That will provide you all you need to thrive with this incredible technology.
3. Get my books: I have several books that go in depth into topics such as AI, programming, and optimization.
4. Consulting: send me a message and we can arrange work with you as a consultant.