Unlocking the True Power of AI: Why Measurement Matters
As artificial intelligence reshapes our world, ensuring that AI applications deliver real business value is more critical than ever. According to Eilon Reshef, cofounder and CPO of Gong, a systematic approach to measurement and evaluation is essential for creating AI solutions that truly excel. This proactive stance not only validates AI investments but also ensures tangible benefits for businesses embracing this technological revolution.
The Crucial Role of Measurement
There’s a saying in the tech world: “You can’t improve what you don’t measure.” This rings especially true when it comes to AI applications. While many tech providers and businesses may focus on integrating AI into their operations, the lack of rigorous measurement can hinder progress. In this age of AI hype, the spotlight should shift toward validating the effectiveness of these solutions. Business leaders are now, thankfully, prioritizing the need to demonstrate the value of their AI investments, and measurement can pave that path.
Consider some of the significant advantages that solid measurement practices can offer businesses:
- Establishing Performance Baselines: Knowing where you stand is the first step to improvement.
- Tracking Progress Over Time: Continuous improvement is only possible with a clear understanding of how you’re advancing.
- Data-Driven Decision-Making: With concrete data, businesses can make informed choices about optimization.
- Demonstrating Value to Customers: Showing incremental value can build trust and bolster client relationships.
By embedding measurement into the process, we can finally shed light on AI applications that have historically functioned as a “black box,” unveiling their true effectiveness and utility.
Approaching Measurement in AI
While generic benchmarks serve a purpose, determining how an AI model fits a specific use case demands a tailored approach. Different challenges require unique metrics. For AI-driven classification tasks, traditional benchmarks like precision and recall offer a solid start. However, for industry-specific applications—be it sales forecasting or legal research—custom metrics that reflect user success perceptions become crucial. For example, legal practitioners might prioritize the relevance of outcomes over the volume of data compiled.
When it comes to large language models (LLMs), measuring quality can become complex. With no absolute "ground truth," nuances of style or context can overshadow factual correctness. In such situations, focusing on the model’s predictive capabilities is often more telling than mere speed or response volume.
A Fresh Perspective with Elo Ratings
At Gong, we’ve taken an innovative route by applying the Elo rating system—originally designed for chesses—to evaluate the performance of our generative AI solutions. Just like how chess players are ranked based on their winnings against others, our AI applications are assessed through a similar competitive lens. Organizations like LMSYS have adopted Elo ratings to create leaderboards for foundational LLMs, setting a standard for comparison.
Think about revered chess champion Magnus Carlsen, who achieved an astonishing Elo rating of 2,882. This numeric representation of his skill encapsulates his dominance in the field, much like how we can quantify AI application prowess.
In evaluating generative AI versions, it’s essential to consider more than just model behavior. Users don’t interact directly with a language model; they engage with a composite application shaped by various components, including prompts and input data. Enhancements to any of these elements can significantly influence overall performance.
Integrating Measurement into Development
To create AI applications that consistently deliver exceptional results, measurement needs to be woven into your development lifecycle. By continuously comparing various versions of your algorithms, you can swiftly identify high-performing iterations.
A useful strategy is to establish a “gold set” of examples. This curated set acts as a constant standard for evaluating performance, speeding up comparison processes and mitigating the need for generating new testing data with each software update.
Once you’ve defined clear benchmarks and achieved measurable outcomes, don’t shy away from transparency—publish these results. This openness supports a constructive dialogue with customers regarding ongoing quality improvements.
As AI continues to advance at a rapid pace, your practices for evaluating and enhancing applications must evolve alongside it. By committing to rigorous, transparent evaluation, you can ensure that your AI offerings remain at the industry’s cutting edge.
The AI Buzz Hub team is excited to see where these breakthroughs take us. Want to stay in the loop on all things AI? Subscribe to our newsletter or share this article with your fellow enthusiasts.
