Model Fine Tuning – A Complete Guide

Fine-tuning an AI model is similar to teaching a student who already knows the basics but needs specific training for a new subject.

For example, if an athlete practices only against beginners, they will struggle against expert athletes. However, if they fine-tune their skills by training with expert athletes, they’ll adapt, improve, and evolve.

In the following sections, we’ll build more on the concept of model fine-tuning by explaining two examples. One example scenario is of a gaming studio that builds an AI model for understanding real-time player behavior by fine-tuning it.

The other example is a real-life case of an AI SEO tool that we have built and fine-tuned for better keyword research. We’ll explain the fine-tuning by applying the concepts of hypothesis testing to prove or disprove its claims.

What is Model Fine Tuning?

Fine-tuning in machine learning is the process that involves:

• Taking a pre-trained model – a model that already understands patterns.
• Adjusting the internal settings to focus on new data.
• Keeping useful information intact while making the model better suited to perform new tasks.

Let’s understand this through an example of a gaming studio that wants to fine-tune its existing AI model to fetch real-time player behavior and data.

A gaming studio built an AI-driven game recommendation system for its open-world, first-person games. The AI suggested in-game quests, collecting items, solving puzzles, and fighting battles based on how the players interacted with the game.

Over the last three months, player engagement levels have started to drop. The recommendations felt repetitive and predictable, and players left the games in between. This made the data team notice a crucial loophole.

While the AI was trained on past player data, it wasn’t adapting to real-time player behavior. Seeing this, the engineers propose a solution in which real-time player behavior data will be included in the new dataset that will be used for model fine-tuning.

The parameters that need to be measured are how a player’s interests evolve and how they approach challenges. However, now, the question arises: When and why do you need to fine-tune a model?

We’ll answer this question in the next section.

When Do You Need to Fine-tune a Model?

In the case above, the gaming studio’s AI model was becoming outdated. It couldn’t recognize when a player shifts from combat gameplay to side quests.

Generalized AI also lacks personalization. While the gaming model had broad datasets to be trained on, it wasn’t optimized for real-time decisions.

Developing a new model is expensive because it requires huge datasets and computation.

The AI model had learned from past player data. However, it needed to be fine-tuned to new and real-time player behavior to stay relevant and up to date. Model fine-tuning will help improve the accuracy of recommendations and keep players engaged.

Since the company already has a pre-trained model, it can use transfer learning instead of starting from scratch. Next, the team collects and prepares real-time player data through interactions like recent in-game purchases, time spent on different missions, frequency of activities, and dialogue choices.

Then, they can clean up the data and feed it into the model to use AI for better decision-making by understanding and uncovering recent patterns. Here’s where model fine-tuning takes place:

• The team can keep some layers frozen (unchanged) to retain previous knowledge.
• Higher layers can be updated with new weights to allow prioritization of real-time trends.
• They can train the model on the new set of player data using gradient descent to optimize recommendations based on freshly received insights.

In the next section, we’ll examine a real-life case in which our experts built and fine-tuned an AI SEO keyword research tool.

Benefits of Model Fine-tuning

Suppose there’s an AI-driven tool that can analyze trending topics using natural language processing (NLP) and machine learning (ML). However, its recommendations aren’t always spot-on. Sometimes, it suggests outdated topics or keywords with low engagement.

The belief here is:

The data collected will help filter and plan content strategies, improving content optimization accuracy and organic traffic for increased ranking in SERP results.

Hence, the improvements can be tested for statistical significance by hypothesis testing for model fine-tuning. Let’s understand how it can benefit them:

Steps to Fine Tune a Model

Since new data trends constantly emerge, the model needs to stay updated with fresh insights. It also improves user engagement metrics with accurate predictions. To successfully fine-tune the model for the AI SEO tool, here are the steps:

Model Fine Tuning Techniques

Fine-tuning is needed when a pre-trained AI model doesn’t perform optimally for a specific use case because it lacks domain-specific knowledge. If there’s a performance gap with irrelevant results, it helps to customize it by aligning the output with user expectations.

While the technique used above was inferential statistics through hypothesis testing, many other techniques can be used for model fine-tuning. Here are some of them:

Tools and Frameworks for Model Fine-tuning

Setting and measuring evaluation metrics are essential to ensure the tool is precisely and accurately deployed. These include measuring how often it predicts relevant keywords and accurately suggests high-ranking ones.

Other than that, understanding how the predictions deviate is also important. To achieve all of this, there are many tools available:

Real-world Applications of Fine-tuned Models

While LLMs have revolutionized the field of NLP, they often require further optimization to handle tasks specific to a certain domain. RAG and fine-tuning are two primary methods for enhancing these models in different domains.

Here are some of these real-world applications:

Challenges in Model Fine-tuning

Building and fine-tuning a model comes with its own set of risks and hurdles. Sometimes, it won’t show continuous updates with new search trends. Other times, it won’t be able to retrieve real-time keywords because it doesn’t cover a wide range of trending keywords.

Let’s understand some of these challenges:

Best Practices for Model Fine-tuning

Fine-tuning turns a stagnant AI into a smart and adaptive system. It is a powerful tool for keeping AI models relevant over time, and it can save development time and costs. However, before deciding whether to implement it at a larger scale, the team needs to have proper practices in place to overcome common challenges.

Here are some of the best practices that can be adopted:

Conclusion

The AI model isn’t just about improving accuracy. It’s more about developing an AI model that can adapt accurately in the long term while being efficient. However, businesses looking to develop an AI model often struggle with performance inconsistencies, scalability limitations, and outdated learning mechanisms.

At Moon Technolabs, our AI development experts don’t let an underperforming AI model slow down your growth. We are a leading AI development company specializing in custom AI model fine-tuning and addressing these challenges with advanced optimization techniques.

Our team is here to help you refine parameters, enhance data quality, and prevent overfitting to ensure your AI solution evolves alongside industry trends, delivering faster insights and improved predictions.

Let’s transform your AI model into a high-performing, data-driven engine for success. Would you like a consultation on fine-tuning your AI model for maximum efficiency? Get in touch with us TODAY.