Once your search UI is live and you have started collecting user data, you can fine-tune your search relevance. This is a post-launch process. Use it after your baseline setup is already working.
Prerequisites
- a Search App with a live, integrated search UI
- analytics data or user feedback to guide your decisions
- a stable baseline setup for Search Fields, Ranking, Stop Words, and Spell Check
See Setting Up Search Fields, Ranking, Stop Words, and Spell Check if you still need to complete the initial setup path.
Expand Queries with Synonyms
Users often search for the same concept using different words. Synonyms help bridge that gap and reduce no-result searches.
Use focused synonym groups and validate your highest-volume queries after changes. Large or loosely governed synonym groups can make debugging harder and can expand query complexity unnecessarily.
See Synonyms for configuration details.
Apply Business Logic with Rules
Rules let you apply conditional logic to a query. Use them when the right behavior depends on query intent, rather than static ranking weights alone.
For example, you can boost support content when a query contains support or promote a specific resource for a branded query.
See Rules for more information.
Use Advanced Ranking Functions Carefully
Function boosts are useful when you need more control than field weighting alone can provide.
- Recency boosting: favor newer content with a date-based function
- Popularity boosting: rank items higher based on a score such as views or ratings
Use these only when you can explain and validate the ranking change with real query examples.
Smart Answers Tuning
Use Smart Answers as a post-launch quality improvement, not as a replacement for core relevance setup. If you enable Smart Answers, review the generated responses with the same discipline you use for ranking changes.
Focus on whether direct answers are accurate, appropriate for the experience, and supported by the indexed content.
Optimization Cadence
Optimization is iterative. Review analytics regularly and use a fixed query set when testing changes so you can compare before and after behavior.
Start by watching metrics such as click-through rate, no-result queries, and queries with poor engagement. Then make small, explainable changes and re-test.