Practical insights on search strategy, architecture, relevance, and performance from real-world implementations.
How well does TypeSafe AI’s JEV handle common search problems? We explore query understanding, term importance, and product categorization through practical tests that reveal useful applications and limitations.
A search engine is one part of a modern search architecture. This article examines how data preparation, retrieval, ranking, and evaluation work together to support reliable search as requirements evolve.
AI agents use retrieved information to guide decisions and actions. This article explores how search architecture provides authorized, current, and relevant evidence throughout a workflow, while managing the latency and cost of repeated retrieval.
This article explores a practical use of LLMs in search relevance work: identifying poorly performing queries for human review. It describes the evaluation pipeline and compares results from a cross-encoder and a generative model using sample ecommerce searches.
Category Routed Aliases can displace the Gateway Anchor Collection as they create collections and change alias ordering. A CRA-native anchor and ZooKeeper reconciliation preserve distributed query coordination as the alias evolves.
A load balancer distributed requests evenly across a 40-node SolrCloud cluster, yet query coordination concentrated on only a few nodes. We trace the unexpected alias-routing behavior behind the hotspot and explain the anchor collection pattern that restored balance.
Longer queries can challenge lexical search. Learn how important-term boosting can preserve query intent, improve relevance, and better match what users are actually looking for.
Hybrid search doesn’t have to mean merging separate lexical and vector result sets. Explore an alternative architecture that uses semantic search for candidate generation while preserving the relevance, business logic, facets, and control of your existing search platform.
Modern product discovery requires more than traditional search alone. Learn how Large Language Models (LLMs) and BM25-based search engines work together to understand shopper intent, improve search relevance, and deliver more effective ecommerce search experiences.
