Most website search experiences are positioned as tools that do the heavy lifting for users to search through large amounts of content. In reality, most traditional search functions are just a filtering tool, using a keyword matching method to create a list of links to resources. Even with these ‘refined’ results, users still have to scroll through hundreds of links, trying to decide which ones are actually relevant and useful.
Let’s take a home improvement website as an example and type into the search bar, “I need a saw to cut wood.” Instead of understanding my question, this outdated functionality just scans through all the pages that contain those keywords, like ‘wood’ and ‘saw’ and leaves me with hundreds of articles to sift through. The frustration here is that:
- These articles are not relevant to the intent of my question.
- I still have to scroll through links to find what I need, which can take quite a bit of time.
- As a potential customer, I am not given any help or guidance towards making a purchase, which is a missed opportunity for the company to convert me from a browser to a buyer.
We’ve put up with these frustrating and inefficient experiences for years because there wasn’t a better alternative. That is, until the recent introduction of generative AI, which can transform the search tool into an intelligent, context-aware assistant that understands what you mean, not just the keywords you type.
Here’s how it works:
AI search understands meaning, not just keywords
One of the most powerful new techniques that can be applied to the standard search experience is retrieval-augmented generation (RAG) which combines two different processes:
- Retrieval:This is where the AI system scans all of the content at its disposable (this might include an organization’s website, documents, and information in internal databases that your organization has granted access to, like Google Drive, OneDrive, YouTube, and more). It assesses all of these sources to find the most relevant content.
- Generation: After retrieval, it then synthesizes the relevant content that it found into a clear, well-structured response using natural language.
With this approach, users no longer receive that long list of links to sort through. Instead, AI search looks at the most relevant content and produces an answer that actually makes sense – whether the user is looking for information, shopping for a product, or needing support.
It keeps context history to support multi-step conversations
AI-powered search does not treat each query in isolation. If the initial input is unclear, the system is capable of asking follow-up questions to clarify intent. For example, if you type, “What are your hours?” into the search bar, the first response might be: “Are you looking for store hours, customer service availability, or something else?” And then it will generate its answer based on the additional information the user provided. This back-and-forth ensures users get the right information without endless rewording and frustration.
Not only can the search tool ask follow-ups to refine the answers, but so can the user. In outdated search experiences, each new phrase or question the user asks starts the search experience over from scratch, with no memory of previous queries. AI-powered search, however, connects the dots between questions. For example, if a user first asks for store hours, receives an answer, and then follows up with, "Can you give me directions?”, the AI understands they are looking for directions to the same store whose hours were just provided. This context retention makes the search experience more intuitive, efficient, and conversational.
Context history can also support personalized interactions based on individual user behavior and preferences across multiple interactions.
AI search goes beyond question answering to help users take action
While context history can help generate personalized guidance and recommendations, AI search can even go a level deeper, using AI agents to directly interact with third-party systems, like CRMs, shipping platforms, and other internal databases. Direct integrations with these systems allow for real-time data to be pulled into the search experience to help users find specific information and act on it.
For instance, if a customer ordered a product online and asks, “When is my order coming?” the RAG technique could retrieve the general shipping and delivery times for all orders from the company's FAQs. AI agents can go a step further and retrieve information specific to that customer to say, “I see your order was placed two days ago on Monday. It is scheduled for delivery by 5 PM on Friday this week.”
A smarter search experience
Traditional keyword search forces users to do the work like filtering results, rewording questions, and searching multiple times to find what they need. AI search removes that burden by understanding, responding, and even taking action on behalf of the user.
Back to our BuildWell Home Improvement example – with traditional search, I typed, “I need a saw to cut wood” and got flooded with irrelevant articles. Now, with AI search:
- It understands I am looking for recommendations and product guidance, not just articles.
- It suggests saws that match my needs (for wood) and provides direct links to each product for an easy purchase experience.
- It uses image AI so I can see what the products look like.
- It can even call an AI agent to check inventory and guide me to the nearest location that has my chosen saw in stock.
This is how AI search transforms website interactions: helping users faster, improving engagement, and driving conversions.
Get in touch with ai12z today for a demonstration on AI search with your website.