What is LLM Fine Tuning, and What Is It Good For? Common Problems Solved

It is good, and not that simple a question. Everybody is using LLMs daily, a lot of applications are built around them, and you do not need fine tuning for 90% of your daily AI tasks. So do you need fine-tuning at all?

Before we answer this question, first of all, let’s try to understand what is fine tuning and what are the alternatives.

How LLMs Work Without Fine Tuning

Usually you use LLMs by prompting them with all you need. You explain your context, you explain your need, you explain how the result should be. After you get the results you usually do a few more iterations to get exactly what you need. Nice, it is super useful. And then you do it again and again.

It’s somewhat similar to hiring for your company somebody smart and extremely knowledgeable, but with one small problem:

They forget everything after each conversation. And you need to explain each time all the context of your every need. Uff…

So it’s not exactly an employee, it’s a kind of consultant who came to do the job, and forget about you. Are consultants useless? Absolutely not. Are they a replacement for your dedicated employees? Again, no.

What is Fine Tuning?

Fine tuning is making an LLM “yours” – to know what is your context, in what way you prefer to get results, to know your company internal documents, etc. It’s what actually distinguishes between an employee and consultant in the first place. But there are also other, more subtle things…

The Cost-Efficiency Factor

You hire people for specific functions in your organization. You decide what skills you need and look for them. So you do not pay for the skills you do not need. If you need somebody to speak to customers, you take somebody who is good at it, but you do not pay the salary of a space engineer or translator from 5 languages – because you do not need it. You save a lot by finding the best match for your position.

Very similar things happen with LLMs. When you pay for the privilege to speak with a “super brain” in the form of the latest and greatest GPT model – you pay for myriads of its capabilities, but actually use very little part of it. 

When you know your task, you can select a much smaller LLM, probably 10-50 times smaller than you are using and “teach it” to do what you need. Again – you can save a lot of time, money and actually stop wastefully warming up the planet by selecting a good enough LLM for your needs.

       Cost of LLM usage is proportional to its size

Teaching Smaller Models Your Specific Needs

But what is tuning, anyway? Can I just select a smaller LLM which will suit my needs? In theory – yes. But in practice small LLMs are not “that smart” to do anything you need, but they are smart enough to “learn” what you need. This process of teaching an LLM the skills you need is called fine tuning.

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Fine Tuning: The Simple Definition

To summarize, fine tuning is the process of adaptation of an LLM for your needs. It’s similar to the initial onboarding you give to your new employee. So, together we get to the conclusion that:

Fine tuning for LLMs is like in-house training for people

When Do You Need Fine Tuning?

Let’s use our analogy above to answer this question. When do you need training for your employees? I can imagine two main reasons, or some mix of them:

Reason 1: Specialized Knowledge Requirements

First and foremost when you do something special, not exactly like others, not exactly as taught in university. I believe you do want to differentiate, after all…

Reason 2: Cost and Resource Optimization

When hiring somebody who knows everything from day one is not feasible for some reason – too expensive, not enough talent available, such a super-hero will get bored quickly doing basic stuff, etc…

When to Use Fine Tuning for LLMs

The same story applies with LLMs:

• Domain-specific knowledge: We need fine tuning when there is your internal organization or domain specific knowledge not available to “off the shelf” LLMs.
• Cost optimization: We need fine tuning when the big and smart LLM is expensive and there is a place to save by using a smaller one.

Conclusion

To sum up our short story – fine tuning is a way to adapt smaller LLMs to suit your specific needs, just like training employees for your unique business requirements. It bridges the gap between generic AI capabilities and your specific use case, offering both customization and cost efficiency.

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