How to Improve Website Search: Smart Search That Understands Meaning

Why Traditional Search Often Doesn't Work

Traditional search usually works like this:

user enters a word → the website searches for that word in the title or text.

For example, an online store contains a product:

“Kitchen faucet with a high flexible spout”

The customer might search for:

“kitchen tap”

or:

“faucet for sink”

or:

“kitchen water tap”

Some of these queries may contain none of the words that the seller used in the product name.

The situation is even worse when there are typos:

“kitchen faucett”

“fauccet”

“kitchin tap”

A person clearly understands what they need. A search engine does not always.

The same situation occurs on a content website.

Suppose the website contains an article:

“How to insulate the exterior walls of a private house”

The user might search for:

“how to make the house warmer”

“how to insulate a house from outside”

“how to insulate a wall”

For traditional search, these are different sets of words.

For a person, they are essentially the same question.

What Is Semantic Search?

To solve this problem, you can use semantic search or vector search.

Instead of comparing only individual words, the system analyzes the meaning of the text.

A special model converts the text into a numerical representation called an embedding.

For example:

“How to insulate the exterior walls of a house” → embedding model → [0.12, -0.31, 0.74, ...]

The user's query is converted in the same way:

“How to insulate a private house” → embedding model → [0.10, -0.28, 0.71, ...]

The system can then determine that the two texts are close in meaning even if they use different words.

This is why vector search is especially useful when users formulate queries in natural language.

What Does This Give the Website Owner?

The main advantage is not the technology itself.

The main benefit is that the user has a much better chance of finding the content they need.

For an online store, this may mean:

• products whose names do not literally match the query;
• products with alternative names;
• results when the user makes a typo;
• similar products;
• finding the right category even if the customer does not know its exact name.

For an information website:

• articles formulated in different words;
• recipes based on a description of a dish;
• instructions for a problem that the user described in their own words;
• articles containing synonyms;
• content that traditional search would fail to find.

In other words, the website no longer requires the user to know exactly how the author named the content they need.

Example for an Online Store

Imagine a small plumbing store.

The catalog contains:

Kitchen faucet with a high flexible spout

The customer enters:

“kitchen tap with a flexible nozzle”

Traditional search may return very few results or none at all.

Semantic search can determine that:

• tap ≈ faucet
• kitchen ≈ kitchen faucet
• nozzle ≈ spout

and show the right product.

Another customer might write:

“I need a faucet where I can direct the water stream”

Such a query may not contain the words “flexible spout” at all.

But the meaning of the query matches the product characteristic.

These are exactly the situations where smart search for an online store becomes especially useful.

Example for a Recipe Website

Suppose a website contains a recipe:

“Chicken fillet in creamy sauce”

The user enters:

“what can I cook with chicken and cream”

Or:

“simple chicken with cream recipe”

Or:

“quick chicken in creamy sauce”

The keywords may be different, but the meaning of the query remains similar.

Instead of searching for the exact phrase “chicken fillet in creamy sauce,” the system looks for content that matches the meaning of the query.

For websites with a large number of articles, recipes and guides, this can be especially useful.

Example for a Home Improvement and Construction Website

Suppose a website contains an article:

“Foundation waterproofing for a private house”

The user enters:

“how to protect a foundation from water”

or:

“what to do so that moisture does not destroy the foundation”

or:

“water near the foundation of the house”

Such queries may be more useful than an exact search for the word “waterproofing.”

The visitor often does not know the professional term.

They simply describe their problem.

And a good search should understand that problem.

An Important Advantage for Older Websites

Implementing this type of search does not necessarily require completely rebuilding the existing website.

This is especially important for small businesses whose websites have been running for many years and are built on an old CMS or a custom system.

There is not always a reason to migrate an old online store to a new platform just to get modern search.

You can keep the existing website and modernize the search mechanism separately.

The old structure:

• products
• categories
• articles
• search

can gradually receive a new search layer:

• existing content
• existing database
• new semantic search

This means that the website content does not necessarily need to be rewritten or migrated.

Existing products and articles can first be analyzed and converted into embeddings, which are then used by the new search system.

What Exactly Is Indexed?

There is no need to create a vector only for the full text of a page.

Different websites can index different types of information.

For an online store:

• product name
• description
• specifications
• category
• brand

For an information website:

• title
• summary
• article text
• tags
• category

For a recipe website:

• dish name
• description
• ingredients
• cooking instructions
• category

The more useful information is available to the search system, the better it can understand what it is working with.

Why You Should Not Simply Replace Traditional Search with Vector Search

In practice, the best results usually come from using several mechanisms at the same time.

For example:

exact match + full-text search + vector search + error correction

This is called hybrid search.

Suppose a user searches for:

“iPhone 16 Pro”

Here, exact matching may be more important than semantics.

But if the user writes:

“Apple phone with three cameras”

semantic search becomes much more useful.

That is why modern search systems can use several signals at the same time.

Search with Typographical Errors

Another important advantage is handling typos.

In a real website, users rarely enter every query perfectly.

For example:

“washing machine”

with a typo instead of:

“washing machine”

or:

“cordless drill”

with a spelling error instead of:

“cordless drill”

Traditional search may return an empty result.

Smart search can first determine that the query is close to existing terms and then perform an additional semantic search.

This is particularly important for mobile users, who often type queries on a phone.

Users Do Not Have to Know the Correct Terms

There is another problem that website owners often underestimate.

A visitor may simply not know the term used in the catalog or article.

For example, a seller writes:

“Thermostatic mixer tap”

while the buyer searches for:

“a faucet that automatically regulates water temperature”

From a human perspective, the query is perfectly clear.

Traditional search may not understand the relationship.

Semantic search can determine that the two formulations are close in meaning.

The same thing happens in medicine, construction, automotive websites, home appliances, cooking, and practically any other subject.

You Can Search by What the Customer Wants to Do

This is another interesting possibility.

The user can describe a task rather than a product.

For example:

“I need a tool for driving screws into hard-to-reach places”

The catalog may contain:

“Cordless right-angle drill/driver”

The customer does not know the name of the tool.

But they know what they want to do.

A good search should allow them to search in exactly this way.

What Happens Inside the Website

From a technical point of view, the process is relatively simple.

First, all existing content goes through an embedding model:

Product → Embedding → Vector → Database

The same process is used for articles:

Article → Embedding → Vector → Database

When the user enters a search query, the system performs the same operation:

Search query → Embedding → Query vector → Search for similar vectors → Search results

Instead of comparing only words, the system compares the positions of texts in a mathematical space.

Where Can the Data Be Stored?

If the website uses PostgreSQL, the pgvector extension can be used for this kind of search.

For example, a product could contain:

• id
• name
• description
• embedding

And an article:

• id
• title
• content
• embedding

The vector can be stored directly alongside the original content or in a separate search table.

This makes it possible to avoid building a completely separate system just for search.

For small and medium-sized websites, this approach can be quite sufficient.

What If There Are Thousands of Articles?

Large articles can sometimes be split into separate fragments.

For example, instead of creating one embedding for an article containing 20,000 characters, you can create several:

• Introduction
• Materials
• Preparation
• Main instructions
• Errors and recommendations

Each fragment becomes a separate searchable object.

This makes it possible to find not only the relevant article, but also the most relevant section of that article.

This is especially useful for:

• guides;
• documentation;
• large reference resources;
• construction manuals;
• recipe websites;
• technical articles.

How Can You Tell That the Search Has Actually Improved?

Implementing a new search is not enough.

Its effectiveness should be measured.

You can store user search queries:

• search query
• number of results
• clicked result

This shows what visitors are actually looking for.

For example:

• “kitchen tap” → 124 queries
• “faucet” → 83 queries
• “kitchen faucet” → 46 queries
• “faucet with typo” → 17 queries

At the same time, you can identify queries for which the website returned no results.

This is very useful information for the website owner.

Sometimes search statistics reveal topics, categories and products that users want to find but that do not exist on the website at all.

Search Can Reveal Weak Points of the Website

Analyzing search queries can help identify:

• which products are searched for most often;
• which categories are missing;
• which product names are unclear to customers;
• which articles are difficult to find;
• which wording real visitors use;
• which queries regularly return zero results.

For example, if dozens of visitors search for:

“how to insulate a balcony”

but there is no suitable article, this is already an idea for new content.

As a result, internal search becomes not only a navigation tool, but also a source of information about what the audience needs.

Search Can Reduce the Need for a Complicated Menu

For a small website, it is not always necessary to build a huge category structure.

Imagine a store with several thousand products.

The user may not know:

Power tools → Drills → Cordless → Impact

It is easier to type:

“cordless drill for concrete”

Smart search can take them directly to the right products.

The result becomes a kind of interface:

“Just describe what you need”

This is much closer to the way people naturally express their needs.

Do You Need Artificial Intelligence?

In this case, artificial intelligence is not being used to generate text or communicate with the user.

It is used as a tool for understanding content.

An embedding model essentially answers a question like:

“How closely are this query and this document related in meaning?”

This means that even a regular online store can use modern AI technology without turning the website into a full-scale AI service.

For the user, everything still looks familiar:

[ What are you looking for? 🔍 ]

But internally, the search works much more intelligently than simple word matching.

How Much Does This Search Cost?

The cost depends on the number of products, pages and search queries.

Embeddings for existing content can be created once and then updated only when the content changes.

For example:

new product → create embedding → save to database

If the product description changes:

description changed → create new embedding → update index

The search query itself is also converted into an embedding, after which the system searches for the nearest results.

For a small website, this can be a relatively simple and inexpensive additional service.

What Else Can Be Added?

After implementing basic vector search, the system can be developed further.

For example:

Typo correction

“washng machine” → “washing machine”

Synonyms

car = automobile = vehicle

Search by specifications

“Apple phone under $1,000”

Filters

• category
• price
• brand
• availability

Ranking

Show the most relevant products or articles first instead of simply showing the first matches.

Natural-language questions

For example:

“What can I cook with chicken, potatoes and cheese?”

For a recipe website, this type of search can be much more convenient than a traditional search by dish name.

What Does the Website Owner Get in the End?

Smart website search is not just another technological feature.

It is a way to make existing content easier to access.

Products do not need to change.

Articles do not necessarily need to be rewritten.

The website does not necessarily need to be completely rebuilt.

What changes is the way visitors interact with the content that already exists.

Instead of requiring:

“Enter the exact name”

the website starts working according to a different principle:

“Describe what you are looking for in your own words.”

That is the main advantage of semantic and vector search.

For an online store, this means a more convenient way to find products. For a content website, it means faster access to the information visitors need. For an older website, it provides an opportunity to improve one of the most important features without completely rebuilding the project.

And for the website owner, the most valuable benefit is the ability to finally make full use of the content and catalog that have accumulated over the years but were previously difficult to find through traditional search.