A/B testing for semantic search involves comparing two versions of a search system to determine which performs better in understanding user intent and delivering relevant results. Start by defining a clear goal, such as improving click-through rates, reducing query abandonment, or increasing the accuracy of top results. Split your user traffic randomly between the control group (existing system, “A”) and the variant group (modified system, “B”). Ensure both groups receive the same distribution of query types and user contexts to avoid bias. For example, if you’re testing a new embedding model, deploy it alongside your current system and route 50% of queries to each. Log detailed data for both groups, including search results, user interactions (clicks, dwell time), and any downstream metrics like conversions.
Next, choose metrics that align with your goal. Common metrics include Mean Reciprocal Rank (MRR) for ranking quality, precision@k (e.g., how many top 5 results are relevant), or query resolution rate (percentage of queries where users found what they needed). For semantic search, also track latency—since more complex models might slow response times. Use statistical tests like a t-test or chi-square to determine if differences between groups are significant. For instance, if variant B shows a 10% higher MRR but a 200ms increase in latency, you’ll need to weigh relevance gains against performance costs. Tools like Google Optimize, Split.io, or custom logging with Python (using libraries like SciPy for statistical analysis) can automate this process.
Finally, iterate based on results. If variant B performs better, roll it out gradually while monitoring for edge cases (e.g., niche queries where the new model fails). If results are inconclusive, refine your hypothesis—perhaps testing a different embedding size or adjusting the training data. For example, a travel app might discover that a BERT-based model improves results for ambiguous queries like “affordable tropical getaway” but struggles with location-specific searches like “Paris museums.” Address gaps by retraining the model with domain-specific data or hybridizing semantic and keyword-based approaches. Document lessons learned and repeat the process for continuous improvement.