In the previous chapter, we were focused on improving the user search experience. We started with using the terms and phrase suggester to correct typos in user queries. In addition to that, we used the completion suggester to create an efficient, index time-calculated autocomplete functionality. Finally, we saw what Elasticsearch tuning may look like. We started with a simple query; we added multi match queries, phrase queries, boosts, and used query slops. We saw how to filter our garbage results and how to improve phrase match importance. We used n-grams to avoid misspellings as an alternate method to using Elasticsearch suggesters. We also discussed how to use faceting to allow our users to narrow down search results and thus simplify the way in which they can find the desired documents or products. By the end of this chapter, we will have covered: