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

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

Users now expect direct answers, not just links. Whether you're using ChatGPT, Perplexity, or another AI search platform, the experience is the same: ask a question the way you'd ask a human and get a clear, natural-language answer.

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

At its core, SGE refers to a new class of conversation-based search 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 AI search platforms 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 fast, trusted responses. The Conversational Search capability within Squiz Funnelback Search 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. Content preparation: Before anyone asks a question, Content Intelligence audits your approved content and prepares it as a validated question and answer database.
2. Understanding the question: Each question is interpreted in the context of the conversation, and the most relevant validated information is drawn together.
3. Answer generation: A Large Language Model (LLM) writes a natural-language answer from your approved content only, within a secure regional cloud environment, with responses starting to stream in seconds.
4. Guardrails and guidelines: Answer guidelines shape the tone and format of each answer. If an answer isn't in your content, a default response you control is shown instead of a guess.
5. Response delivery: The user receives a direct answer and can continue the conversation naturally.

This approach is a key part to how Conversational Search – a capability of Squiz Funnelback Search – brings generative AI into a controlled environment grounded in your approved 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:

• Accurate: The answer needs to reflect the current truth - not last year’s policy, last quarter’s intake date, or an older set of requirements. AI search platforms can surface outdated pages if they still look authoritative.
• Consistent: The same question should not produce multiple versions of the answer across your site. If two pages both look “official” but don’t match, AI search platforms can’t reliably pick the right one - and may blend them into a response that’s technically wrong.
• Explicit: Don’t make the reader (or the AI) infer what you mean. Vague language, buried conditions, and undefined terms force interpretation. In an answer-first experience, that can often result in a generic summary that sounds plausible but isn’t actionable.⁠
• Complete: The page that “owns” the answer needs enough detail to stand on its own. If key steps, exceptions, or document requirements live across PDFs, older announcements, and supporting pages, AI won’t get the full picture – not because the information doesn’t exist, but because it’s scattered.⁠
• Structured: Structure is what makes information reusable. Clear headings, logical sections, and consistent page patterns make it easier for AI to extract the primary answer, the conditions, and the next steps as one coherent response – instead of pulling fragments that lose meaning.⁠

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

Start with these steps:

• Start with a focused topic slice: 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 what’s missing, unclear, or inconsistent: Identify where key details are vague, scattered across pages and PDFs, or described differently in different places (including inconsistent terminology).
• Fix the issues that break answer quality: Create a clear, prioritized plan to improve explicitness, consistency, and structure - rewriting vague content, aligning terminology, and adding summaries to help AI interpret content accurately within your selected topic.

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 Conversational Search. And to help you maximize the success of this AI implementation, Squiz Content Intelligence is included to support AI readiness auditing, smoother rollout, and continuous improvement over time.

Ready to begin your AI implementation journey? Want advice on how to get started?

Book a 30-minute chat with a member of our team.