AI Fine-Tuning is Forever
Whether it’s learning a new musical instrument or learning a new language, we know that practice - aka training - makes perfect. When we have a mid-life crisis and decide to run a marathon or climb Mount Everest (even if it’s just getting to Base Camp), veterans tell us how important it is to start training right away! In essence, the average human gets better with training.
But what about the experts?
Consider professional athletes that need to perform at their peak season after season for years on end - think basketball players like Steph Curry or Lebron James, or gymnasts like Simone Biles. They spend as many as six hours a day retraining themselves, picking up new skills, learning new moves, and just getting better!
That brings us to AI.
Applications based on AI/ML models and LLMs are just like elite athletes! Once data scientists train a model or an AI program and release it, their job isn’t over. In fact, you could say that their job has just begun. That’s because, once a model is trained, just like elite athletes, it must be continuously retrained - or fine-tuned, to use industry speak - to make sure that the model continues to respond well and doesn’t “drift”.
In other words, fine-tuning is forever.
As AI models like ChatGPT, Llama, Claude, and others become more integrated into our lives and start to tackle a vast and growing number of use cases, AI builders must continuously monitor the quality of their models' responses and keep fine-tuning them.
There are several reasons why this is important. We’ll briefly discuss each one, to understand why fine-tuning can make or break an AI program.
The Nature of Language and Context
Language is not static; it evolves over time. New words emerge, meanings shift, and usage patterns change. An AI model trained on data from a few years ago may produce outdated or irrelevant responses today. Continuous fine-tuning, using fresh, high-quality datasets allows AI models to adapt to these changes, ensuring they understand and respond to language in its current form.
For instance, an AI model that serves teenagers or young adults will want to continually incorporate recent slang - by being fine-tuned with fresh, high-quality datasets generated by humans that are similar to the target demographic - to provide the model with much-needed context.
Maintaining Relevance
The world around us changes every day. If you’re using a search engine to look up current events and your search engine provider decides to use an AI model to answer user queries, the model must be retrained and fine-tuned every day, using the latest, emerging information, to provide accurate and reliable responses.
Or, consider a health and wellness platform that relies on an AI model to give medical advice. The model must incorporate the latest research findings, dietary guidelines, and medical treatments. Also, the model must never provide responses that could result in harm or be open to misinterpretation by the average human being. So the health provider must continuously monitor the model’s responses along a number of different dimensions - and continuously fine-tune it with new high-quality data. This will help ensure that users receive the most current and helpful health advice, improving users’ health outcomes.
Improving Accuracy and Reducing Bias
AI models are also not immune to inaccuracies and biases present in their training data. These biases can lead to skewed or unfair responses, potentially harming users and damaging the credibility of the AI. Continuously monitoring the AI’s responses and regularly fine-tuning it with high-quality, diverse and representative data will help mitigate these problems.
Better User Experiences
A key aspect of user satisfaction with AI models is the quality of their interactions. Users expect clear, relevant, and helpful responses. If an AI model gives you confusing or unhelpful answers three times in a day, you’re probably never coming back to that AI model or vendor.
For example, a financial services company using an AI model for customer support would need the model to understand complex financial terminology, the latest market developments, company trends, and more - much of which changes every day, and sometimes, every hour. The AI model must therefore be trained and continuously fine-tuned with different, high-quality datasets so that it responds to users’ questions and issues with the latest, up-to-date, relevant answers.
Adapting to Specific Use Cases
Different industries and applications require AI models to possess domain-specific knowledge. Generic models may lack the expertise needed for specialized tasks. For instance, a multinational company deploying an AI model as a virtual assistant in different countries in South America must account for unique cultural contexts and Spanish language variations, which tend to evolve and change over time.
Continuously fine-tuning the model with region-specific data, including local dialects and evolving cultural references, enables the AI to interact more naturally and effectively with users, by making interactions feel more personal and relevant.
Conclusion
Unlike software a decade ago, AI models can’t just be built and released annually. Once an AI model is released, its outputs must be continuously monitored to maintain relevance, accuracy, and adherence to ethical standards.
AI builders that want to attract new users - and retain existing ones - must therefore plan to retrain or fine-tune their AI models frequently, and possibly even daily, by using high-quality data.
If you’re an AI/LLM builder that needs high-quality datasets turned around in 24-48 hours - or you’re a domain expert anywhere in the world that would like to serve the world’s AI builders as part of the e2f team, please contact us today.
