In my last post, I explained how AI is helping reinvent Knowledge-Centered Service (KCS) by eliminating the friction in knowledge creation. However, speeding up content creation is only part of the solution—if users can’t find what they need quickly, even the best content won’t have an impact. That’s where semantic search plays a role.
If KCS builds the haystack, then semantic search is how we find the needle, and it represents a fundamentally different approach than what most organizations use today.
The Keyword Trap
Traditional keyword-based search has long frustrated knowledge workers. Why? Because it requires users to guess the exact terms the system expects. If they search with slightly different language or make a typo, they are likely to miss relevant results entirely. Search administrators end up spending countless hours building synonym dictionaries, configuring stop word filters, and tweaking relevance rules just to make search usable.
Consider this real scenario: a customer trying to find information about Washington-area flights might type in the airport code ‘WAS’, but traditional search engines treat ‘was’ as a stop word and ignore it. The user receives irrelevant results, assumes the knowledge doesn’t exist, and creates a support ticket. The system failed not because the information wasn’t there, but because it couldn’t bridge the gap between what the user typed and what they meant.
The Semantic Breakthrough
Oracle’s AI-first strategy replaces these fragile methods with semantic search, powered by large language model embeddings. With semantic search, the system recognizes that ‘WAS’ might refer to an airport and keeps it in context. This kind of contextual intelligence makes search not just more precise but also more intuitive and forgiving.
Semantic search works by understanding the meaning behind words rather than just matching exact terms. Instead of looking for literal keyword matches, it represents both the user’s query and all the knowledge content as vectors – essentially points in space based on meaning. When someone searches, the system compares the meaning of the question to the meaning of every sentence in the knowledge base and surfaces the most relevant results, even if the words don’t match exactly.
This enables semantic search to better handle typos, synonyms, complex questions, and queries in different languages. A search for “password reset” will surface articles about “credential recovery” or “account unlock procedures” because the system understands these concepts are related.
Setting Realistic Expectations
If this sounds like a game-changer, it is. But if you’re just starting out, it’s important to set expectations for both search admins and users.
Since semantic search doesn’t return results based on exact keyword matches, this can be a change for those used to scanning for bolded terms or direct hits. Instead, results focus on meaning, which may require reading the content to fully understand its relevance. Search admins in particular should recognize that results might look unfamiliar or less obviously relevant at first, but often turn out to be very useful once you engage with the content.
My advice: be patient, give the system time to learn, and trust that the results will improve the experience over time. For end users, especially customers, the transition generally goes more smoothly since their queries are grounded in context and intent from the beginning.
Next in this series, I’ll examine how Oracle uses generative AI to deliver natural-language answers across both agent and customer channels. From retrieval-augmented generation (RAG) to governance best practices, we’ll explore what it truly takes to make AI-generated answers safe, effective, and trustworthy in a service environment. Continue reading part 3 here.