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Metrics for Recommendation

Comprehensive presentation on Metrics

Metrics are crucial in interviews to demonstrate a candidate's data-driven mindset and ability to measure and improve performance, while in a job, metrics provide the data-driven foundation for informed decision-making, goal setting, and performance evaluation. Lead the way with data literacy. Let your metrics do the talking.

Metrics for Search Effectiveness

Measure Search Relevance

Metrics are essential for evaluating search engine performance by measuring factors like click-through rates, bounce rates, and time spent on pages. These data points provide insights into user behavior and inform algorithm improvements to enhance search results. Use Data driven decisions and experimentation.

AI Assistant Metrics

Evaluate AI Assistants

Comparative Analysis of AI Assistants: Overall Performance, Response Quality, and User Impact. Metrics are vital for assessing the performance of AI assistants by quantifying factors like accuracy, response time, and user satisfaction. By continuously evaluating these metrics, developers can identify areas for improvement and optimize AI assistant capabilities.

AB Testing

Test Static Websites

Create multiple versions of a page and randomly serve different versions to visitors to determine which version perform better. Using data driven ab testing improve user experience, conversion rates and websites effectiveness through technqiues like feature flagging and split testing. Two Sample T Test, Paired T Test, Chi Square Test, Mann Whitney Test, Wilcoxon signed Rank Tests are common test for ab testing.

AB Experiment for Chatbots

Test Dynamic Conversations

Create multiple versions of a chatbot interaction such as different greetings, questions or response options to identify the most effective approach. Unlike static website, chatbot experiment focus on dynamic conversation and can use metrics specifc to tasks e.g. task completed, # of steps to complete task, Customer effort saved, CSAT etc. Using task specific metric help compare chatbot and AI Assistants.

Experiment with Data Products

Create new data and determine what works

Create multiple approaches to create new data product/signals/summary. It require testing with multiple chunking, embedding, algorithm to process and create new data. Experiment with data products has 2 part - 1. determine data is useful and then 2. present to user to determine whether it is usable. The first part use human evaluation as well as accuracy/fact check to ensre data is correct and then ab experimentation is done.