AI search goes beyond keyword matching to understand meaning. Semantic search finds relevant results even when exact terms don't match.
AI Search Components
- Embeddings: Convert text to vectors
- Vector database: Store and search vectors
- Hybrid search: Combine semantic + keyword
- Re-ranking: Improve result ordering
Search Quality Metrics
- Precision: Relevant results / total results
- Recall: Found relevant / all relevant
- MRR: Mean Reciprocal Rank
- NDCG: Normalized Discounted Cumulative Gain
Is semantic search better than keyword search?
For natural language queries, yes. Hybrid approaches combining both often work best. Test with your specific use case.
Practical example
An internal search assistant finds a relevant document that belongs to another team. Apply access rules before placing its text into the answer context.
What to check
Test known relevant documents, empty results, stale records, and permission-filtered results.
Limits and tradeoffs
Semantic similarity alone does not establish relevance, freshness, or authorization.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.