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Metrics and KPIs are fundamental parts of any product strategy. They require a whole another level of insight and granularity when it comes to the ever changing and growing world of applied Generative AI applications.

Understanding Gen AI metrics and KPIs is crucial for AI product managers for several reasons:

1. Performance Evaluation: Metrics and KPIs provide a quantitative way to assess the performance of AI models. This evaluation helps in understanding how well the models are meeting the desired objectives and where improvements are needed.
2. Continuous Improvement: With the right metrics in place, product managers can identify areas of improvement and iterate on the AI models. This iterative process is essential for staying competitive and adapting to changing user needs.
3. User Satisfaction: Metrics related to user experience and satisfaction provide insights into how well the AI product meets the expectations of end-users. Positive user experiences are key to the success and adoption of AI products.
4. Ethical Considerations: Metrics related to bias, fairness, and transparency help product managers ensure that their AI models are ethical and do not disproportionately impact specific demographics or exhibit biased behavior.
5. Risk Mitigation: Monitoring metrics such as false positives/negatives and robustness helps in identifying potential risks and mitigating them before they adversely affect the users or the business.
6. Decision-Making: Gen AI metrics and KPIs assist product managers in making informed decisions about the direction of the AI product. Whether it’s scaling up, optimizing, or addressing issues, data-driven decisions lead to more effective strategies.
7. Communication with Stakeholders: Metrics provide a common language for communication between product managers and other stakeholders, such as developers, executives, and customers. Clear communication based on data fosters understanding and alignment on goals.
8. Resource Allocation: Knowing which metrics are most relevant to the business goals allows product managers to allocate resources effectively. Whether it’s investing in data quality improvement or optimizing model performance, resources can be directed where they are most needed.

Let’s break down these metrics into two major categories:

1. Technical metrics
2. Business metrics

1. Technical Metrics

As a Product Manager, if there’s one type of product where the PMs should learn and get familiar with technical metrics, it’s for the Gen AI applications (I would argue that this is true for all applied AI products, not just Gen AI). Without understanding how the underlying foundation model is performing for your usecase, or how your fine tunings or RAG (Retrieval Augmented Generation) applications perform, against various benchmark criteria, it’s impossible, or rather not useful, to move onto crucial user and business metrics, because you don’t know whether you have a valid and trust-worthy technical product in the first place.

Technical metrics comprises of industry standard benchmarks for evaluating Large Language Models and the end-user applications on difference criteria like Fluency and Coherence, Accuracy and Factual outputs, Reasoning and Understanding, Safety and Bias etc

Here, we will divide the Technical metrics into 3 sections for clarity and granularity:

1. Model Metrics
2. Application Metrics
3. System Metrics

Below is a quick reference sheet for all there of these metrics:

1.a. Model Metrics

Here we get familiar with evaluating the LLMs with metrics like SuperGLUE, BLEU, ROUGE, METEOR, CIDEr (keep following this page for updates to these metrics).

1.b. Application Metrics

Here we see application metrics like Error rate, Latency, Accuracy range, Safety score, Bias, Groundedness, Relevance, Coherence, Fluency.

1.c. System Metrics

Here we see system metrics like Data relevance, Data & AI asset and reusability, Throughput, System latency, Integration and backward compatibility, Real-time updates, Cost, Compute and Infrastructure sustainability.

2. Business Metrics

Now we come to the all important user and business metrics, that Product Managers are all familiar with. Here’s is a quick reference sheet for metrics specific to applied Generative AI applications – Adoption rate, Frequency of use, Session Length, Queries per session, Query length, Abandonment rate, User satisfaction.

Conclusion

Follow this page as the metrics get updated based on the newest evaluation techniques for Large Language Models and Generative AI applications. Below is the combined reference sheet for Generative AI Metrics and KPIs – feel free to download and share!

REFERENCES:

https://cloud.google.com/transform/kpis-for-gen-ai-why-measuring-your-new-ai-is-essential-to-its-success
https://learn.microsoft.com/en-us/azure/ai-studio/concepts/evaluation-metrics-built-in