What is the best way to measure AI product performance?

An AI product would be measured ultimately on whether its cost-beneficial to a given use case. For that you want to measure effectiveness ( accuracy) and efficiency (latency, cost etc.). All metrics that are measured should eventually answer this question - whether the product is making/saving more money than what we spend on it.

To effectively measure AI product performance, establish measurable objectives aligned with desired outcomes like enhanced user experience or increased efficiency. Select actionable, relevant metrics like accuracy or ROI to evaluate progress towards these objectives. Establish baselines for initial performance levels and compare them with industry benchmarks to assess competitive standing. Regularly gather feedback from users and stakeholders and closely monitor product behavior through various analytical tools to identify areas for improvement. Employ testing methodologies such as A/B testing to validate performance and guide product optimizations, regularly revisiting and refining your measurement processes to maintain relevance.

In my experience, the SMART framework is invaluable, but it's equally important to continuously revisit and refine these objectives as the AI product evolves. For instance, while initially aiming to reduce response times with a customer service chatbot, you might later find that enhancing the quality of responses is equally crucial. The objectives should be dynamic, reflecting ongoing learnings and user feedback.

Start with the business goals. Ignore the technology, don't think about AI, just focus fully on the problem you're trying to solve. It can be helpful to map out where you are today, what the ideal future state would be, and importantly what the benefits would be if you reach that future state.

In addition to industry benchmark, if the use case is about automation or human augmentation, you can use the current accuracy rate of the process/activity in its current state. People have this false perception that AI should 100pct accurate to be worth it. Reality is that it's never (rarely ? ;-)) 100pct accurate, so people are quick to use that argument to dismiss progresses, this can be put in perspective if AI performance is compared to metrics obtained currently without AI (like error rate for instance).

The relevant metrics should be identified based on the application and domain. For example, a chatbot offering health-related suggestions should have a high recall, since no relevant information should be missed (i.e., fewer false negatives). In such a scenario, the F2 (rather than F1) score may be considered. On the other hand, for a chatbot recommending research papers, a relatively lower recall may be acceptable. Moreover, a comprehensive evaluation should take multiple metrics into account. Finally, when an AI model is integrated into a product, the time taken for an inference also matters. For example, a chatbot should start responding within a few seconds. On the other hand, relatively more time may be allowed for an image generator.

First off, we gotta know what we want our AI to achieve. It's like setting a goal for a workout - do you want to run faster or lift heavier? Once we've got our goals, we need some way to check if we're getting there. That's where metrics come in. Think of them as the scales or the stopwatch in our workout analogy. For AI, we have some fancy-sounding metrics like 'accuracy', 'recall', and 'F1-score'. But don't get bogged down by the jargon. If we're talking about a chatbot, it's just about checking if it's understanding users right and if people are actually finding it helpful. So, in short, with AI, it's all about setting clear goals, picking the right ways to measure progress, and always being ready to tweak things based on what we learn

The choice of metrics should not only reflect the product's performance but also its impact on the overall business or operational goals. For instance, while precision and recall are vital for a chatbot's performance, measuring its impact on overall customer loyalty or sales can provide a more holistic view. It's about aligning AI performance metrics with broader business KPIs.

Metrics are quantitative or qualitative indicators that can be tracked and evaluated over time to assess the progress, quality, and impact of the AI product. Depending on the type of AI product, the metrics can vary widely, but some common categories are accuracy, efficiency, usability, reliability, fairness, and ethics. For each category, there may be several specific metrics that can be used, such as precision, recall, F1-score, latency, throughput, error rate, user satisfaction, retention, engagement, availability, robustness, bias, transparency, and accountability. The chosen metrics should be relevant, meaningful, measurable, actionable, and aligned with the AI product goals and KPIs.

Is your product generating consistent revenue growth and low churn... then its adding value.. if not.. its not.. dont need to write a masters thesis on performance indicators.. also lets be honest the AI products are nearly all just GPT or Palm wrappers..

For any chosen KPI, there must be a baseline or the minimum expected performance. This baseline will serve as the least expected level of performance from which you can progressively iterate to improve the performance and progress toward achieving your goals as indicated by the trends in your KPIs. A properly chosen baseline can greatly help in understanding any trends provided by your metrics. Once these are set, define expected benchmarks that would serve as milestones along the development plan. These benchmarks would be indicative of any previous activities or "sprints", and more importantly, be a clear indicator of sustainable indicators for the continued progress of the project. Successfully meeting those benchmarks would bode well.

It helps to have measurements associated with whatever process the AI solution is replacing. That way, you will have direct insight into the ROI of the new solution. Leaders will ask, "What's the delta?" and you'll have the answer.

Establishing baselines and benchmarks is crucial, but it's also essential to understand the context behind these numbers. For example, if your chatbot has a high retention rate, delve deeper into why users return - is it due to satisfaction, or because they couldn't resolve their issues in the first interaction? Contextual analysis of benchmarks can reveal deeper insights.

An often overlooked fact is that models accuracy should also be tested against business objectives. Some use cases do not require high levels of accuracy to show business value, you just need to improve on current system efficiencies (which are often poor to start with). You need to test your model's accuracy with respect to the estimated impact it will have on improving some business process/ project goal. Often small changes to existing processes can result in large financial/ other benefits. In these cases it is better to have a slightly less accurate/ simple/ transparent/ stable model which is easy to maintain than a super complicated expensive model that only produces marginally more returns and needs to be updated frequently.

A clear way to capture AI product performance is through the value created for the end users. While this may be application specific, there are some common trends, such as improving productivity or reducing the average response times. Companies integrating AI into an existing process, have the benefit of comparing the performance with vs. without AI. However, a simplistic before/after comparison is problematic, since many other variables not related to the use of AI may have changed, (seasonality, UX improvements, different user/customer cohorts), calling into question the attribution. This is best handled as an A/B/n test, dividing the tasks equally/randomly between the existing process and various versions of the AI algorithm.

This is where qualitative insights complement quantitative data. Observing how users interact with your AI product, especially through qualitative methods like user interviews or focus groups, can uncover nuances that numbers alone might not reveal. These insights are invaluable for iterative improvements.

This part of model evaluation is probably the most important in my opinion in terms of value measurement. Measuring the accuracy of your model does not prove that it has any real value or business impact and hence has little value in and off itself in this regard. Evaluating whether your model had a real impact in a business setting is however very impactful. Different methods can be used (depending on the domain) such as A/B testing, prospective randomised controlled trials, retrospective observational studies such as case matched pairs studies as well as qualitative studies such as surveys or interviews. Often the only way to show impact is by prospective studies which are sometimes not possible after the fact, or too expensive.

Feedback can be gathered through various channels, such as surveys, ratings, reviews, comments, complaints, suggestions, or testimonials. Behavior can be tracked through metrics, such as usage, retention, engagement, conversion, satisfaction, loyalty etc. These data sources can provide insights into how the AI product is perceived, valued, and adopted by the target audience, as well as how it impacts their outcomes, needs, and preferences. Monitoring feedback and behavior can help to validate the initial assumptions and hypotheses, test the effectiveness and efficiency of the AI product, and discover any gaps or errors in the functionality, usability, reliability, or accuracy of the AI product.

Feedback and behavior give you the 'soft metrics' that quantitative KPIs often miss. For example, if users are consistently bypassing a feature in your AI tool, that's a behavioral indicator that the feature may not be as useful or intuitive as you thought. Similarly, look for patterns in customer reviews and ratings. Are users praising or complaining about the same things? This qualitative data can provide invaluable insights for product refinement.

Value measurement is not a once off exercise. You have to be able to show ongoing value. Ideally reports should be generated routinely to show ongoing value. Ongoing value calculations/ reports might be different from initial value studies, for pragmatic reasons such as cost involved in generating these studies/ reports. Ongoing reports don't need to be as accurate as initial reports. Without ongoing value reported, AI models should be decommissioned as operational costs for AI models are typically very high.

Here, I outline a user-focused approach using Evals to identify the most effective output among multiple models for a specific use case. Evals are a structured technique to assess and contrast various prompts, gauging their efficacy in obtaining desired responses from a GPT model. The aim is to refine prompts to better resonate with designated tasks, enhancing the GPT model's performance in a scenario. The procedure entails: - Setting up an Eval template with necessary prompts and model configurations. Inserting test instances for processing through the prompts. - Evaluating outcomes to determine prompt precision. - Documenting performance to improve prompt effectiveness for a task within an AI project.

Testing should be an ongoing process, not just a one-off and experimentation is at the heart of optimizing AI products. Continuous A/B testing, for example, helps in adapting to changing user behaviors and preferences. It's important to create a culture where experimentation is encouraged and learning from 'failures' is seen as a step towards optimization.

You can use A/B testing, randomized controlled trials to compare different versions of your AI product or features, and measure how they affect your metrics. You can use various methods and tools to design and run experiments and tests, such as multi-armed bandits, offline evaluation, online evaluation, or prototyping. Use simulation, synthetic data, and user feedback to test your AI product's robustness, reliability, and usability in various situations and contexts. By conducting experiments and tests, you can identify the strengths and weaknesses of your AI product, validate your assumptions and hypotheses, and optimize your AI product performance based on data and evidence.

In my humble perspective in order to ensure the effective functioning of AI models in a production environment, it’s crucial to adopt a Quality Assurance (QA) approach. For example, this involves rigorous testing of the training data and the hyperparameter configuration data. Additionally, the external AI model itself should be subjected to thorough examination. It’s also important to verify the operationalization component of the AI system, which is the code responsible for deploying the AI model into production.

It's important to remember that AI product performance is not just about numbers; it's also about the user experience and ethical considerations. In all AI projects, I emphasize the importance of considering the ethical implications and ensuring that the AI behaves in a fair and unbiased manner. This holistic view is critical for long-term success and acceptance of AI products.

What is the best way to measure AI product performance? Before anyone can measure AI product performance, you need to define what you want to achieve. Set SMART goals that have clear KPIs. Don't confuse activity with performance progress - your product delivers or it does not.

Measuring the performance of generative AI tools isn't just about technical benchmarks. While metrics like BLEU for text translation or F1-score for understanding text are important, what really matters is the value these tools bring to your business. Consider how fast the tool generates outputs, how easily it scales, and how much computational power it needs. It's also crucial to pay attention to real-world factors like user engagement and ROI. And if you're looking at leaderboards like HuggingFace, don't get lost in the numbers; focus on what those metrics actually mean for your specific needs, such as cost savings, speed, or improved customer experiences.

Finally, measuring AI product performance is not a 'set it and forget it' task. It's an ongoing process that requires a feedback loop involving real-world data, user behavior, and business impact. Always keep an eye on the evolving landscape of AI ethics and regulations, as compliance and ethical considerations can significantly impact both your product and its performance metrics.

The best way to measure AI product performance is to use a combination of metrics that align with the product's goals and to track performance over time to identify trends and areas for improvement. Example: For an AI product that recommends products to users, you might measure accuracy (how often the product recommends products that the user likes), precision (how often the product's recommendations are relevant to the user's interests), and recall (how often the product recommends all of the products that the user likes). You might also track metrics such as user engagement and conversion rates. PS. This text was written by ChatGPT to get the badge =)