To A/B test vector-based search against keyword-based search, start by splitting your user traffic into two groups. One group uses the vector-based system (which relies on semantic similarity), and the other uses the keyword-based system (which matches exact terms or phrases). Ensure both systems handle the same queries simultaneously, and track metrics like click-through rates, time spent on results, conversion rates, or task completion times. For example, if users search for “affordable winter jackets,” the keyword system might prioritize products with those exact terms, while the vector system could include items labeled “budget-friendly cold-weather coats” based on semantic proximity. Use statistical tests to determine if differences in performance are significant.
Next, focus on isolating variables that could skew results. Ensure both systems return the same number of results and operate under similar latency constraints, as slower response times might negatively impact user engagement regardless of relevance. For instance, if the vector-based search relies on a GPU-accelerated database while the keyword system uses a simpler index, the speed difference could confound the results. To address this, either optimize both systems for comparable performance or account for latency in your analysis. Additionally, log user interactions—like query reformulations or abandoned searches—to gauge frustration or satisfaction. For example, if users of the keyword system frequently retype queries, it might indicate poor result quality.
Finally, analyze the data with a focus on specific use cases. If your application serves technical documentation, measure how often users find correct answers on the first try. For an e-commerce platform, track sales conversions from search results. Suppose the vector system shows a 15% higher click-through rate for ambiguous queries like “light laptop for work,” where keyword search might miss synonyms like “portable ultrabook.” However, keyword search might outperform for precise terms like “iPhone 15 Pro 256GB.” Segment results by query type to identify strengths and weaknesses. Run the test long enough to capture diverse scenarios and avoid seasonal biases. Tools like A/B testing platforms (e.g., Optimizely) or custom analytics pipelines can automate metric collection and significance checks.