In the “Inside AI Search” series, Quasiris shows what lies behind the individual features of modern AI-powered search. This article looks at a feature that may seem small at first glance but can significantly change how people interact with search results: Smart Snippets.
A traditional search displays a title, a link, and an excerpt from the retrieved document. Smart Snippets go one step further. They attempt to make the exact part of the content that matches the current question visible, or to generate a short, understandable answer from it.
This reduces one of the biggest points of friction in search: finding a relevant document does not yet mean finding the relevant information.
What Are Smart Snippets?
A snippet is a preview of a search result.
In a traditional document search, it might look like this:
Travel Expense Policy 2026
“… business trips must, in accordance with the applicable policy, before departure …”
For the question:
“What is the hotel allowance for Berlin?”
this excerpt is of little help.
A Smart Snippet can specifically highlight the passage that relates to the question or summarize the information concisely.
The result might, for example, read:
Hotel costs in Berlin are covered up to the defined maximum amount. Details can be found in Section 4.2 of the Travel Expense Policy.
This gives the user guidance without losing sight of the source.
Why Snippets Are Becoming More Important
Search results are increasingly not just scanned but evaluated directly on the results page.
An eye-tracking study published by Microsoft Research in 2026 examined how people interact with search pages containing generative summaries. The generated content received significantly more attention than traditional results, even though users continued to interact with traditional links.1
This has an important implication for enterprise search and product search:
The content visible directly in the results list strongly influences whether a user perceives the information as relevant.
A poor excerpt can make a good result appear unusable.
A good snippet, on the other hand, can explain why the result is relevant before the document is even opened.
Smart Snippets Are Not Just Any AI Summaries
A generated summary of an entire document and a Smart Snippet serve different purposes.
A summary answers:
- What is this document about?
A good Smart Snippet, on the other hand, answers:
- Why is this document relevant to my current query?
This is a crucial difference.
The same product page may require a different excerpt depending on the query.
For:
“How long does the battery last?”
the snippet should highlight battery life.
For:
“Is the device compatible with Windows 11?”
the same product page is relevant, but different content should be displayed.
Smart Snippets are therefore query-dependent.
The Source Must Remain Visible
Generative AI can turn long documents into short, easy-to-understand texts. However, this very capability creates a new responsibility.
Users must be able to distinguish between:
- The original content
- An extracted excerpt
- An AI-generated summary
The more important the information, the more relevant this transparency becomes.
In 2026, for example, Google more closely integrated its generative search interfaces with direct links, source previews, and further content. The aim is to continue giving users access to the underlying websites alongside the summarized information.2
The same principle applies to enterprise search.
A Smart Snippet should shorten the path to the source, not hide the source.
Grounding Determines Reliability
When Smart Snippets are generated, they should be based on the content actually retrieved.
This so-called grounding connects the generated output to a verifiable source.
Current systems rely on precisely this approach. Google, for example, describes grounding as a way to base model responses on retrieved information and connect sources directly to the answer.3
In enterprise systems, this source may be a product database, a wiki, technical documentation, or an internal search index rather than the public web.
The underlying principle remains the same:
First find relevant information, then generate the presentation from it.
Not Every Result Needs Generated Text
Smart Snippets do not necessarily have to be generated by a large language model.
With structured data, direct extraction can often be better.
For example, if a user searches for:
“Delivery time Product X”
and the delivery time is available as a structured attribute, the system should display this information directly.
Generative methods become particularly useful when:
- Relevant information is spread across several sentences
- Long documents are being searched
- Content needs to be summarized clearly
- Different wording is used
- A natural-language question needs to be answered
The appropriate method therefore depends on the data type.
Smart Snippets in Practice
There are many possible areas of application.
Knowledge Management:
- A policy is found, and the passage relevant to the question is displayed directly.
Technical Support:
- A manual provides the relevant solution step directly in the search result.
E-commerce:
- Product attributes that match the current query are highlighted.
Customer Service:
- Service agents immediately see the relevant passage from a knowledge article.
Document Search:
- Long PDFs can be assessed without having to open every document in full.
Smart Snippets can therefore save time, particularly when working with large collections of information.
What Should Be Measured During Implementation
Success cannot be determined solely by whether users click on results less often.
Fewer clicks can be positive because the information is already visible. However, they can also mean that the snippet is not convincing.
Useful questions therefore include:
- Does the snippet provide the information being sought?
- Is the source clearly identifiable?
- Do the snippet and the original content agree?
- Do users reach the information they need more quickly?
- Are important limitations presented correctly?
- Does the system recognize when a reliable short answer is not possible?
The last point is particularly important.
A system that does not have a sufficient basis for an answer should not attempt to fill the gap creatively.
Conclusion: Smart Snippets Shorten the Path from Result to Information
Search quality does not end with ranking.
A result must also make it clear why it is relevant. Smart Snippets create precisely this connection between the results list and the actual content.
Used correctly, they can save users time, provide guidance, and preserve access to the original source.
The Quasiris Search Cloud supports Smart Snippets to provide relevant information matching the query directly within the search experience.
Would you like to explore how Smart Snippets could work with your product data or documents? Discuss your use case with our search experts.
We show you which AI features can also create added value for you in our overview of the AI Features of the Quasiris Search Cloud.
Transparency notice: This image was generated using artificial intelligence and is for illustrative purposes.
Sources
1 Allawati, S., McKay, D., Sanderson, M., Thomas, P., & Trippas, J. R. (2026). An eye tracking study: Are AI overviews changing search behavior? Microsoft Research.
https://www.microsoft.com/en-us/research/publication/an-eye-tracking-study-are-ai-overviews-changing-search-behavior/
2 Budaraju, H. (2026). 5 new ways to explore the web with generative AI in Search. Google.
https://blog.google/products-and-platforms/products/search/explore-web-generative-ai-search/
3 Google. (2026). Grounding with Google Search. Google AI for Developers.
https://ai.google.dev/gemini-api/docs/google-search
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