Customizing Language Models: Fine-Tuning vs. Prompt Engineering

Crafting Precision in AI: Unveiling the Divergence between Fine-Tuning and Prompt Engineering for Language Model Customization.

In the rapidly evolving landscape of artificial intelligence, there’s a notable surge in interest and activity surrounding generative AI. The question arises: Why this rush? What’s the driving force? The answer lies in the transformative power of customizing Large Language Models (LLMs). Businesses are increasingly captivated by the potential these models hold, specifically when tailored to their unique needs.

Imagine a language model finely tuned to your company’s specific requirements, adept at understanding industry nuances. A model that not only replicates your good practices but also is able to validate if your processes are compliant with your business and industry. An internal consultant on your demand 24/7. Tools like this are not just theoretical; they are a practical aspiration, promising a revolution in how businesses communicate and operate in the digital age.

If you want to know how you can navigate this generative AI journey, dive into this blog post. Here, we will present to you the two main methods that are currently used to customize LLMs: Fine-Tuning and Prompt Engineering; explaining What, How, and When to use each one of them.

 

A Duo of  Methods

When it comes to customizing Large Language Models (LLMs) for internal data, two approaches stand out: Fine-Tuning and Prompt Engineering. Let’s explore these methods without getting too technical.

Fine-Tuning

Fine-tuning is a meticulous process resembling a performance upgrade for your LLM. The steps involved in this method are the following:

  1. First, set up a data pre-processing pipeline to transform your data/documentation. Note that LLMs, like GPT models, learn more efficiently when trained with questions and answers. For better results, you might need to re-write your documentation so the relevant data is provided in a question-answering format (or ask chatGPT to do it).
  2. Then, train the model with the pre-processed data/documentation
  3. Test the model and iterate over the previous 2 steps until you get the desired results.
  4. Finally, pose questions to your model, and witness it respond with tailored precision.

Prompt Engineering

With Prompt Engineering, the LLM doesn’t need to be retrained. It’s like having a dynamic conversation with your LLM, instructing it on the fly for context-aware responses. All relevant information is passed to the model on the prompt.

  1. Begin by indexing all your data/documents into a vector database for easy retrieval.
  2. When a new request arises, create a pipeline to:
    1. Search for the most relevant data
    2. Pass it in the prompt
    3. Adjust the prompt as needed – this is the step you might need to iterate over
  3. Get the answer

 

In the realm of LLM customization, Fine-Tuning, and Prompt Engineering offer powerful ways to unlock the potential of your internal data. Whether you’re refining precision or orchestrating dynamic conversations, these methods enable your LLM to shine in harmony with your unique information landscape.

Choosing the best method for you it’s not trivial. They both have pros and cons and everything depends on your priorities and constraints. But don’t worry, we are here to help you make an informed decision, just take a look at the next section.

 

How to Choose Wisely?

There’s no correct answer to the question “Which method is the best?” regarding customizing LLMs. In the short term, one may appear less expensive, but over the long run, it could prove to be more costly. The other can perform better when there is a large volume of data to pass to the model but it might underperform for smaller datasets. So let’s dive into the variety of factors that might influence your decision and analyze the pros and cons of each method.

Data Volume

If you’re dealing with a small collection of one-page documents, regard it as a relatively small dataset. Chances are, this quantity might not suffice for effective fine-tuning of a Large Language Model (LLM). Additional efforts and investment in data augmentation techniques may be necessary to enhance the training process. In this case, I would recommend going for the Prompt Engineering method.

In case you have a large volume of data, I have another question for you: How much data is needed to answer a single question? Some requests require a lot of background information to be answered, e.g. if I ask for a summary of my physics book, I need the model to have access to the entire book. However, if I’m just looking for the 1st Law of Motion, my model will only need to have access to the paragraph of the book where that law is explained.

If you need to pass a large volume of information to your model, Prompting Engineering won’t be the best solution, since the prompts are limited and, even if they weren’t, the prompts would be too expensive to make this solution a good choice. Therefore, for this case, go for the Fine-tuning method.

If your model doesn’t require extensive access to background information to address your inquiries, either approach can integrate seamlessly into your system. Therefore, consider the remaining factors for a tiebreaker.

Information Transience

If you are dealing with temporary data or data that might be constantly updating, the best option would be the Prompt Engineering method. In this case, you just need to guarantee that the indexation process runs as frequently as your data is updated and you’ll have a customized LLM that is always looking for the most recent data and ignoring the deprecated one.

Alternatively, you’d have to consistently fine-tune and test your LLM, a process that could extend beyond the duration of the data period.

Time to Market

If you’re eager to swiftly launch an MVP for solution validation, Prompt Engineering is your optimal choice due to its quicker testing and customization capabilities. However, this doesn’t necessarily imply it’s the most suitable long-term solution. But it definitely enables you to deploy your solution, gather user feedback, and conduct other experiments, in parallel.

Data Governance

If you possess sensitive information that shouldn’t be accessible to all users, it’s crucial to exercise extra caution to prevent information leakage. Consider that you have to deal with data that should only be available to specific users based on their roles within your organization. This is how you can address the Data Governance issue:

  • Fine-Tuning Approach: When you train an LLM model with specific information, there’s a risk of data leakage in its responses. To manage data governance, fine-tune a separate model for each role (or group of roles with identical data access permissions). When a user logs in to make a request to the AI, you’ll invoke the corresponding model. This implies maintaining as many models as there are roles within your organization, which may pose challenges for model maintenance.
  • Prompt Engineering Approach: This method is more straightforward as it involves filtering the data accessible to the user while searching for the most relevant information. By doing so, you ensure that the data included in the prompt’s content doesn’t disclose any private information, making it a more streamlined approach compared to fine-tuning.

 

Costs

If you’re seeking to determine the more cost-effective method, there isn’t a definitive right or wrong answer. The fine-tuning method involves a higher initial investment as it requires payment for the development of the solution, which is a lengthier process compared to the Prompt Engineering approach.  However, passing data in the prompt, as it’s done in Prompt Engineering, makes the prompt longer and, therefore, more expensive. Consequently, in the long run, the Fine-tuning method may prove to be more economical. Yet, for the first version of an MVP, the Prompt Engineering method could be the optimal choice.

 

Conclusion

As businesses navigate the transformative potential of LLMs finely tuned to their unique needs, the decision becomes nuanced. Fine-tuning offers a meticulous upgrade, demanding an initial investment but promising long-term cost-effectiveness. On the other hand, Prompt Engineering provides agility, especially beneficial for rapid MVP launches.

The variables influencing this decision span data volume, information transience, time to market, data governance, and costs. Whether you prioritize precision, dynamic conversations, or swift validation, understanding these factors guides a judicious choice. If you’re not sure how to prioritize these factors, let us help you make the right decision for your business by booking a meeting with us.

In this blog post, we aimed to help you decide between Fine-tuning and Prompt Engineering, but if you’re considering integrating generative AI in your business for the first time, you should be aware of the potential risks. If that’s the case, we recommend you check out this video on our YouTube channel.

Do you want to further discuss this idea?

Book a meeting with Paulo Maia

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