Are you contemplating whether AI fine-tuning aligns with your business objectives? In this article, I examine the benefits of fine-tuning AI, drawing on insights from my firsthand experiences and the expertise of prominent AI pioneers like Andrej Karpathy and Ilya Sutskever, co-founders of OpenAI.
First, let's demystify what we mean by "fine-tuning". AI fine-tuning involves refining a pre-existing AI model to excel in a specific task or domain. Consider ChatGPT by OpenAI: originally a general AI model, OpenAI fine-tuned an LLM into a conversational AI assistant which responds to your questions in a friendly, informative manner. This same underlying LLM could just as easily be tuned to adopt a sassy and sarcastic tone, as Elon Musk's Grok demonstrates, or to specialise in the legal domain, as pursued by Harvey AI.
In essence, fine-tuning AI allows you to customise and optimise an AI model to meet specific needs and excel in targeted applications.
Have you ever found yourself frustrated when the AI you're using doesn't quite grasp your intentions? You've likely resorted to adjusting your prompts, hoping that by EMPHASIZING CERTAIN WORDS or crafting elaborate instructions with examples, the AI will finally provide the desired responses. But even then, they can still miss the mark, and when a new AI model emerges, your previously successful prompts may no longer yield the same results, leaving you trapped in a cycle of trial and error.
It doesn't have to be this way. AI models should be reliable enough for you to trust that they will consistently perform well for all your users, at scale. The key to achieving this is through fine-tuning, which enables AI models to deeply understand your instructions and intentions, fostering a level of trust that is essential for your business.
The specific instructions and desired outcomes from your fine-tuned AI model will vary depending on your industry and goals. For instance, if you're developing a customer service chatbot, you can fine-tune the model to embody your brand's unique voice, tone, and values. Similarly, if you're building an AI agent that utilises function calls or tool use, fine-tuning will dramatically improve the accuracy and efficiency of your AI's decision-making process.
While general models undoubtedly have immense value for prototyping and demonstrations, and their capabilities are indeed impressive, fine-tuning is the crucial step that bridges the gap between a prototype and a production-ready, reliable AI solution.
Picture this: you've built your core AI product around an external provider's APIs, and suddenly, the unthinkable happens. The APIs are taken down due to a critical system failure, usage costs skyrocket due to unforeseen issues, or rate limits are imposed, preventing you from serving all your users. This leaves your business in a limbo state.
These scenarios highlight the inherent risks of relying on external providers for your AI's foundation. By fine-tuning your own AI model you take a powerful step towards building an AI solution that is truly yours, reliable, and secure.
Moreover, fine-tuning your own AI model allows you to keep all your data within your own walls which is crucial for protecting both your IP and any private user data. In an era where data privacy and security are paramount, having full control over your AI's data flow is vital.
Imagine you're currently using OpenAI's most capable model, GPT4-o (at the time of writing). For every 1 million tokens processed, you're likely paying around £55. Now, consider the alternative: fine-tuning a Llama3 70B model and hosting it through a provider like Fireworks. Suddenly, your cost for the same 1 million tokens drops to a mere £7. If you're currently using GPT-4-Turbo, the cost difference is even more pronounced. At the time of writing, 1 million tokens with GPT-4-Turbo would set you back approximately £110.
If your AI is running inference around the clock, self-hosting your fine-tuned model can be incredibly cost-effective. For £56 per day, you can get unlimited tokens from your fine-tuned Llama3 70B model. This predictable, flat-rate pricing structure allows you to scale your AI solution without the fear of unexpected costs or usage limitations.
How critical is speed for your AI-driven product? OpenAI's recent release, GPT4-o, is touted as their fastest GPT-4 model yet, delivering tokens at a respectable rate of 70 tokens per second—surpassing the reading speed of any human. But what happens when your application involves complex agent-like interactions, requiring multiple function calls and tool usage before delivering a response to the user?
This is where the benefits of fine-tuning become particularly evident. Fine-tuned models, being tailored for specific use cases, allow you to select the smallest possible model that still maintains the high performance and reliability expected in a production-grade AI. Smaller AI models can perform inference significantly faster. For instance, the Llama3 70B model can process over 330 tokens per second, and smaller models, such as Phi3 4B and Llama3 8B, can achieve even greater speeds.
This enhanced processing capability opens up new possibilities for intricate AI agent system architectures, making it feasible to execute more complex reasoning steps before responding to the user.
Fine-tuning AI is a strategic business decision that gives you a competitive edge. It significantly impacts the effectiveness, efficiency, and scalability of your AI-driven solutions.
Without fine-tuning, you remain at the mercy of closed-source API providers where hand-typed prompts are your key IP. With fine-tuning, you take ownership of your AI and your IP is real, tangible data which can be invested in over time to create your own increasingly powerful AI models which your competitors could only dream of.
If you are curious on whether fine-tuning AI could work for you, please do get in touch for a small chat. Otherwise, if you're interested in our process around how we can help you fine-tune your own models, you can check this page out here.
Best,
Dan Austin
