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10 ways to know it’s fake AI search
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You think your search engine really is powered by AI? Well maybe it is… or maybe not. 

Here’s a dirty industry secret: some companies actually outsource the problem. Behind-the-scenes, people are manually writing rules and adjusting results to make it appear that it’s semantic search. It looks like AI search, they call it AI search, but it’s not really AI under the hood. 

Real AI understands natural language and the intent behind queries. It’s like ChatGPT, but it’s trained on your data so it won’t deliver incorrect or misleading results. Plus, it looks like real search — a list of results and filters to help you find what you’re looking for. Under the covers are machine learning algorithms that can automatically unlock performance, improve search results, and significantly reduce the amount of work you need to configure search results. After all, if the search engine understands customer intent out-of-the-box, there’s much less for you to have to set up.

So, how can you know if it’s real semantic search or not? That’s where this guide can help. We’re here to separate the fakers from the makers! You know it’s fake AI search when…

1. You’re still adding synonyms

With AI search, the only time you’ll need to add a synonym is for terms that are totally unique to your business. You won’t need to create synonyms for common vocabulary. If you need to add synonyms, that’s because it is still keyword search behind the scenes, not AI. Oh and note that for every synonym you add, there are dozens you won’t know about in the long tail that would automatically just work if you had real semantic search! 

2. It doesn’t work in other languages

Why is it that your semantic search works great for English, but your French, Japanese, and German sites don’t perform as well? Vector search technologies work for any language. In fact, you can use semantic search technology with unstructured data such as images, audio, video, and it works remarkably well. If it’s underperforming in other languages, it might not be real semantic search.

3. Common typos fail

Vector-based search handles common typos out of the box, so if your search engine is still confused that “vaccum” means “vacuum”, it’s likely fake AI.

4. You’re still optimizing for keywords

Are you borrowing a keyword stuffing strategy from SEO circa 1998 to make sure your on-site search works? Then you’re probably not using AI search. Semantic search vectors understand the meanings of words so you don’t have to perform keyword stuffing or other unnatural search engine acts. 

5. Long tail searches are poor

Many companies follow the 80/20 Pareto Principle by only optimizing results for top queries (head queries) on their site. Optimizing for the bottom 80% (long tail), lower-frequency searches is too time intensive, right? Well… it wouldn’t be at all time intensive if you had real AI which improves site search performance for all 100% of your long tail content.

6. Descriptive and question-based searches don’t work

Your search engine may hate it when visitors search for “affordable dress shoes” or “splitting headache remedies.” Searches like these confuse simple keyword-based search engines. Real AI based engines automatically understand these queries to return the right values.

7. You’re still writing lots of corrective rules

The best way to ensure your customers get good search results is to write hundreds of custom corrective, or relevance, rules, right? For example, you might need to add a rule that explains to the search engine that “usb-c” “usbc” and “usb  c” are the same things. Or that “dress shirt” means a fancy shirt, not a dress. With real AI search, relevance rules are a thing of the past. 

8. They call it “explainable AI”

AI is often called a “black box” or “opaque” because the predictive algorithms can be inexplicable. So, if a company can tell you exactly how their AI arrived at a result, it may be a warning flag. AI models are inherently extremely complex and often include millions, or even billions, of data points during inference. Frequently, they are not explainable. If they claim they are explainable, they need to be able to backup that claim by opening the black box and show which model is used, and how results are derived.

9. It’s tone deaf

With real semantic search, you should be able to search for “a TV that doesn’t suck” or “a tea to help me chill”. In English, both of these terms are common slang and convey a feeling or tone. Keyword-only search engines will fail entirely, but semantic search engines will understand the sentiment used in many common phrases for different languages. 

10. It still works, even if the underlying data quality is bad

As the Harvard Business Review put it, “Poor data quality is enemy number one to the widespread, profitable use of machine learning.” Just because semantic search is amazingly smart, doesn’t mean it can make sense of a website’s poor structure, messy metadata, or mucky formatting. A real AI search vendor may need to work with you to ensure your site data is optimal for machine learning to do its magic. 

Ready for real, end-to-end AI search?

Our combo keyword and AI-powered semantic search solution is coming soon. It includes end-to-end AI including natural language processing (NLP) on the front end, AI-powered retrieval, and Dynamic Re-ranking to automatically re-rank results based on your site’s data. 

Incredible results, significantly less effort, no smoke and mirrors. Sign up to be notified when it’s available.

About the authorMichelle Adams

Michelle Adams

Chief Revenue Officer at Algolia

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