Guide has begun to use semantic search as a way to generate the most accurate search results possible based on the intent and context of user search queries. Unlike search methods that find literal matches to keywords, semantic search captures the meaning of search queries, helping end users and agents search for and locate content without prior knowledge of the exact keywords to use.
What is semantic search?
Semantic search uses artificial intelligence (AI) to process and understand the full meaning and intent of language used in search queries, similar to the way a human would. Specifically, machine learning (ML) and natural language processing (NLP) technologies work together in semantic search to help the search engine understand the user’s intent when they submit a search query. Understanding what the user is actually looking for, regardless of the keywords they use, helps the search engine return and rank the most relevant results.
For example, with semantic search, end users and agents can ask natural language questions instead of worrying about which keywords to use in order to get the best results. New agents can ask “how do I start using Guide?” instead of trying to think of the best keywords to use or the sequence of keywords to use in their query. With semantic search, they can enter their question in the way that makes sense to them, and still see the most relevant articles at the top of the results.
With semantic search, Guide is taking another step towards providing agents and admins a high-deflection help center with an intuitive search experience.
How semantic search works
Guide search has historically relied on keyword matches between queries and content (for example, articles, community posts, or external records). However, it didn’t capture the “meaning,” or semantics, of a query. Semantic search, on the other hand, deploys natural language processing to understand the content of the search query and content. This helps the search engine identify complex patterns that are otherwise missed. In many cases, semantic search is able to capture similarity even when there is not a strong word overlap between the query and indexed content.
Relying on language models, semantic search can translate queries and help center articles into vectors (a numerical representation), and measure the distance between them. Articles closer in the vector space are considered to be more similar by the language model. The language model is trained on many text examples, and through these examples has learned how to accurately interpret the meaning of text accurately.
When semantic search is used, the search engine boosts the relevance of semantically better matches. This pushes the most relevant content, based on intent and context of the search query, to the top of the search results. Each time a search is performed, the search results are ranked not only based on keyword matches but also boosted by semantic matches.
How Guide uses semantic search to improve the search experience
Semantic search offers a more intuitive search experience that lets help center visitors search using their choice of terms. Since Guide has begun to incorporate semantic search, we’ve observed an increase in search quality metrics such as mean reciprocal rank (MRR) and click through rate (CTR). Specifically, we found that MRR improves 7% on average with this first iteration of semantic search for English Zendesk help centers. We’re also seeing more searches with clicks.
By improving the search relevance, and ranking the most relevant results on top, semantic search produces the following help center improvements:
- End users can find the information they are looking for more easily, increasing the deflection power of your help center.
- Agents can be more efficient as they can find answers more quickly.
The degree of impact for semantic search depends on the search behavior of the user. Semantic search has particularly positive benefits for longer search queries, but still improves relevance for all types of search.
Understanding the rollout plan
As we expand our AI model to cover more search channels, we will continue to roll out semantic search into additional areas.
Specifically, the semantic search rollout will be phased into content type, language, and search channel. See the following tables for the current and planned rollout of semantic search based on these parameters.
Rollout by content type
|Content type||Coverage status|
Rollout by help center search type
|Help center search type||Coverage status|
Rollout by search channel
|Search channel||Coverage status|
|Help center search engine results page (SERP)||Covered|
|Request from article suggestions||Planned|
|Unified search API||Planned|
|Articles search API||Will not be supported|
|Posts search API||Will not be supported|
|Web Widget help center search||Planned|
|Knowledge in Agent Workspace||Planned|
Rollout by language
|English (all variants)||Covered|
|French (all variants)||Planned|
|Traditional Chinese||Not planned|
How to check if your help center is enabled for semantic search
We’ve begun our semantic search implementation for Guide customers. As the rollout continues, we will continue to expand the feature to all Guide customers. You can use this procedure to determine whether your help center is currently enabled for semantic search.
- Perform a search in your help center.
- On the search results page, open Inspect element in your browser.
For example, in Chrome, right-click the search results page, select Inspect from the menu, then click the Elements tab.
- Select the <head> tag, then examine the
If the element is set to "false" (as shown in the image below), semantic search is not yet enabled in your help center. If the element is set to "true," then semantic search is enabled.