Blend lexical, semantic, and behavioral signals to improve search ranking
No sign-up required • Free to try
Build semantic search with embeddings, hybrid retrieval, rerankers, and feedback analytics
Map candidate generation, ranking models, features, and real-time feedback loops
Blend lexical, semantic, and behavioral signals to improve search ranking
Common questions about ai search relevance architecture generator
Run BM25 and vector search, then rerank with a learned model. Weight signals based on experiments.
Add spell correction, synonyms, entity detection, and query rewriting. Use behavioral data to refine.
Track NDCG, CTR, recall, and zero-result rate. Run offline judgments with golden queries.
Use semantic search for sparse queries and fall back to lexical for precise terms. Add fallback content.
A/B test ranking changes, monitor guardrails, and keep rollback plans. Observe latency impacts.
Signing up costs nothing and every feature is included. Only AI generation is metered, in credits.