Large Language Models (LLMs) have revolutionised the AI landscape, and have turned the attention of the public to modern AI capabilities. In a nutshell, they are AI-powered systems designed to understand and generate human-like text based on input data. These models have become indispensable tools for enterprises seeking to enhance user experiences, automate processes, and drive innovation across various industries. From chatbots and virtual assistants to content generation and sentiment analysis, LLMs now play a pivotal role in shaping the functionality and capabilities of modern digital products, and the tech is expected to advance rapidly over time.

With an ever-expanding choice of options, brands must navigate the AI landscape with precision and a future-ready mindset. Otherwise, they risk wasted energy, time, and, most importantly, finances in the pursuit of a product that in the end doesn’t fulfill its promise. Not the type of decision anyone can afford to rush!

The first step on your AI journey

It may sound obvious, but the first step towards choosing the right LLM is to define your goals with care, so laying the groundwork for informed decision-making. Then, start by scoping out the most powerful models in the market. For anyone who’s struggling, turn to your digital partners – if they‘re worth their salt they should be informed enough to match the tech that’s out there with your business’s goals.

Next up, test the models rigorously to gauge their performance and suitability for your specific needs. Remember to take time to consider core variables like traffic, cost, and multi-modality capabilities like vision, sound, and spatial as you do so. If your selection fails to meet expectations, consider the timing. In the rapidly evolving landscape of AI, sometimes patience is required – it’s not an exaggeration to say that breakthroughs really are always around the corner when it comes to AI. However, if a model does prove its worth and aligns with your requirements, it‘s time to delve deeper.

Then, analyse the inner workings of the chosen model. Understand the prompts and mechanisms that drive optimal performance. This insight will serve as the foundation for customising the model to align more closely with your objectives. By generating and curating a dataset of positive and negative interactions, you can fine-tune a smaller model to surpass its off-the-shelf counterpart in a specific task. This strategy is particularly effective with open-source or open-weights models and allows for greater flexibility in model customisation.

Investment should improve performance quality

While this approach may entail additional costs, we’re finding that more often than not the investment yields significant returns in terms of performance enhancement. Platforms like Azure offer fine-tuning capabilities for certain models from OpenAI, providing an avenue for optimising performance within budgetary constraints. Finetuning open models needs additional expertise, but there are plenty of vendors providing the compute for it.

Scaling down the model size offers two distinct advantages. Firstly, it enhances cost-effectiveness, allowing for efficient allocation of resources. Secondly, it improves inference speed, crucial for real-time processing applications where every millisecond counts. The process of selecting the optimal language model for your digital product requires strategic foresight and, in a landscape that can advance or turn on a dime, the willingness to adapt.

By following these steps and leveraging the expertise of digital partners, nothing should stop you from carving out a piece of the AI landscape for your brand.

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This article originally featured on The Drum and is available here.