What is Search Generative Experience (SGE) and what does it mean for your website?

For decades, digital search revolved around keywords. Users would type fragmented sentences (e.g. “library opening hours,” “apply for passport”), get served a ranked list of links, and then click, compare, and compile information.

But Search Generative Experience (SGE) is changing how people interact with digital content and, as we explored in this blog, conversational AI search is on the rise.

Users now expect direct answers, not just links. Whether you're using ChatGPT, Perplexity, or another AI-powered enterprise search tool, the experience is the same: ask a question the way you’d ask a human and get a clear, natural-language answer. You can see this difference in the comparison graphic:

To navigate this new era of online content discoverability, you need to understand what’s behind it: SGE. So, how does it actually work?

Skip ahead:

• Defining Search Generative Experience (SGE) and how it works
  • Why Search Generative Experience (SGE) matters so much today
  • The anatomy of this experience
  • How Retrieval-Augmented Generation (RAG) works
• Getting started with Search Generative Experience (SGE)
• Your next steps

At its core, SGE refers to a new class of AI-powered search tools that:

• Allow users to ask questions in natural language (e.g. instead of “library opening hours”, you can ask “What are the university library’s opening hours on weekends?”)
• Use natural language processing and generative AI to synthesize information from multiple sources into a single, summarized response
• Still include links to source materials or traditional search results for those wishing to dig deeper or verify the information

Beyond known, standalone tools like ChatGPT, this shift is also happening inside websites, with organizations starting to implement SGE-style search on their own domains.

This  allows employees and external users to ask questions and receive instant, trusted responses. The Squiz Conversational AI Search feature is an example of an SGE.

SGE represents a fundamental shift in expectations. As content discovery is increasingly streamlined, the burden of doing deep research falls to the tech, not the user.

To understand how SGE delivers this experience, let’s look at what’s actually happening behind the scenes.

A robust SGE typically includes:

• A conversational interface: It accepts natural language input and displays responses in a chat-style or expanded answer format
• Attribution: It cites where the information came from, so users can refer to the sources for verification
• Fallbacks: It defaults to traditional search results or asks clarifying questions when no high-confidence answer is available

Beyond these, more advanced SGE tools also use a Retrieval-Augmented Generation (RAG) architecture. This is crucial to ensure SGE tools don’t make up answers from scratch. The information is always drawn from trusted content; not whatever the AI model learned from the open web or hallucinations it has created.

One key way to reduce the risk of hallucinations in SGE tools is by using a RAG approach.

Instead of asking the Large Language Model (LLM) to answer based on its entire training data, RAG workflows restrict the model to a limited set of approved content. Here’s how it works, step by step:

1. Smart retrieval: When a user submits a query, the system retrieves the most relevant, authorized content. This includes structured and unstructured content across your site, intranet, or any other integrated systems (e.g. social media platforms).
2. Prompt pairing and customization: The retrieved content is then paired with a customizable prompt template, controlling the tone, format, and fallback behavior of the AI response (e.g., what to say when no answer is found).
3. Answer generation within your environment: Large Language Models (LLM) generate an answer using your content only - never referencing information from other online sources. .
4. Built-in accuracy checks: A second AI agent verifies the response’s accuracy  to source content, ensuring no hallucinations.
5. Response delivery with full visibility: The user receives a direct answer with source attribution and can continue the conversation naturally.

This approach is a key part to how Squiz Conversational AI Search (powered by Squiz Funnelback Search) brings generative AI into a controlled environment grounded in verified content. So organizations can mirror the conversational experiences users expect, while retaining accuracy, compliance, and brand voice.

For a closer look at how we do this, explore this blog.

For organizations, SGE creates both an opportunity and a risk. The opportunity is to deliver faster, more satisfying experiences to users. The risk is that those experiences could return hallucinated answers or misleading summaries due to inaccurate, outdated, or unstructured content.

SGE isn’t powered by AI magic alone. The quality of your content is what ultimately determines its success or failure.

High-performing SGE relies on content that is:

• Clear and direct: Answer user questions explicitly, using language they actually use. Avoid vague, convoluted, or unnecessarily long explanations.
• Structured and semantic: Use headings, bullet points, and consistent language. Add metadata to help AI interpret and summarize responses.
• Consistent across pages: Stick to a shared vocabulary. For example, don’t mix “student portal” and “student hub” to describe the same thing.
• Maintained and current: Outdated content can lead to inaccurate answers. Make sure high-impact pages are regularly reviewed and updated.
• Attributed: SGE works best when users can verify the information they see. Link back to sources and clearly indicate where content came from.

Even if you haven’t launched conversational AI search yet, your content is already being interpreted by external AI tools. You should future-proof your digital experience by improving content quality now.

Start with these steps:

• Define a content “slice” to start with: A focused, high-impact content area (like FAQs or your help center) that’s public, high-value, and easy to update is an ideal pilot for auditing and optimizing content for SGE.
• Map common user questions to existing content: Does the content answer the top questions users actually ask?
• Audit your content for clarity and structure: Know where your key information lives and who owns it. Then, make sure it is readable by both people and AI.
• Recommend fixes: Create a clear, prioritized plan to improve clarity, consistency, and structure - rewriting vague content, aligning terminology, and adding summaries to help AI interpret content accurately within your selected slice.

Extra step: Prepare your systems to support source attribution by using schema.org markup (like ‘FAQPage’ or ‘HowTo’) and keeping URLs clean and consistent. If you're using an enterprise search tool, ensure each item includes metadata like title, author, and source. This helps AI generate trustworthy answers with clear citations.

We explain this framework in further detail in this blog.

SGE isn’t just a better search bar. It’s a reflection of a larger trend toward AI-first user experiences. For organizations, the challenge now is to build content and systems that support this shift, delivering clear, useful, and trustworthy information.

At Squiz, we’re helping companies get ahead of this discoverability curve with Squiz Conversational AI Search. And to help you maximize the success of this AI implementation, we offer a dedicated consultancy framework that supports content readiness, smooth implementation of conversational tools, and continuous improvement over time.

Ready to begin your AI implementation journey?