The generative AI space is growing at a breakneck pace, and the need to keep pace with the competition has never been more important. If you’re investing in a large language model, a form of generative AI, or exploring brand messaging services to make the most of this modern trend, it is vital that you have a system in place to measure your success against the competition.
We’ve had the opportunity to work with a variety of clients in this space at Key Light Collective, and one thing is certain: if you want to benchmark your AI effectively, you need to first ask yourself the right questions and measure what really matters!
Defining Your Key Performance Indicators
In order to effectively measure your AI’s success against the competition, it is vital that you define your key performance indicators. Not all generative AI is created with the same end goal, and sweeping metrics simply aren’t going to cut it. As you consider how to conduct competitive benchmarking for generative AI presence, make sure your chosen indicators align with your strategic objectives.
Accuracy is a key performance indicator, and it is often overlooked. How does your AI model perform at generating responses that meet your end goals? For customer service, is it how often your AI model answers a question correctly? For a creative model, is it how well your AI model meets your brand guidelines?
Another key performance indicator is latency. How long does it take your AI model to create a response? If your AI model is too slow, then regardless of how accurate your model is, your end-user experience is going to suffer.
Finally, cost efficiency is a key performance indicator that can’t be forgotten. How much does it cost you to run a state-of-the-art AI model, and how does that impact your end application of it?
Selecting the Right Competitors and Models
The selection of the right competitor and model is a matter that requires strategic thinking. When considering how to conduct competitive benchmarking for generative AI, it’s important to evaluate a range of potential competitors and models:
Choose three types of competitors, namely, industry competitors who use the same type of AI, industry leaders who set the standard within the industry, and open-source models that can give you alternative options.
Do not restrict yourself to the obvious, especially when you’re looking at the pros and cons of the different options that can solve the same problem. For example, the best solution for insights may not come from the same industry, but from an industry that faces the same problem but in a different context.
Lastly, open-source options such as those offered by Hugging Face, Meta, etc., can serve as a great benchmarking option, and also give you a perspective on whether the solution is worth the price tag.
Conducting Qualitative and Quantitative Analysis
One important consideration is understanding how to benchmark generative AI against competitors to truly gauge your model’s strengths and areas for improvement.
After selecting the right options, the next step is to conduct a qualitative and quantitative analysis of the different options. Quantitative analysis is a must for any technical team, as they will need facts and figures that can prove the solution’s capabilities. Quantitative analysis is conducted by testing the options with parameters such as response time, token efficiency, error rate, etc.
The output that is generated is not the right output unless it sounds right, is contextual, and also has the right voice and tone. For this, we suggest that you do a qualitative analysis by testing each of the solutions against the right set of use cases and asking subject matter experts to analyze each of the outputs without revealing which solution was used to generate that output.
Moving Forward with Standardized Evaluation
The generative AI space is still evolving, and so is the evaluation criterion. Industry leaders are increasingly discussing and seeking out generative AI benchmarks as a way to standardize performance assessments. The big players are working towards creating a standard benchmarking criterion, and we will hopefully see that in the near future. Until then, however, it is essential that you keep your benchmarking process fluid.
We also suggest that your competitive analysis needs to be revisited at least once a quarter. New models are being released regularly, and what was considered state-of-the-art six months ago may not even be table stakes today. Keeping abreast of your benchmarking will not only keep your AI strategy competitive but will also ensure that you make the most informed decisions regarding when you should upgrade or change your strategy.
Want to develop an effective benchmarking framework for your generative AI strategy? We’re here to help at Key Light Collective. We have significant experience in assessing the efficacy of AI in several industries. Let’s discuss how competitive benchmarking can help your AI strategy!