When people use a search engine, they usually only see the final step.
A search box.
A query.
A list of results.
The complicated parts are hidden behind that interface.
Once you try to build a search engine yourself, you quickly realize that the internet is not a clean database. It is a constantly changing collection of websites created by millions of different people, companies, and systems.
Some pages are written to answer questions. Some are created to sell products. Some exist mainly because someone wanted to rank higher in search results.
A search engine has to figure out the difference.
That is the problem I started working on while building Slick, an independent search engine focused on customization, privacy, and giving users more control over their search experience.
The first challenge is finding the web
Before ranking results, before machine learning, and before semantic search, there is a simpler problem:
How do you collect the documents?
A search engine needs its own view of the web. That requires systems that can discover pages, fetch content, process information, remove duplicates, and keep everything updated.
Crawling sounds simple at first. You request a page, save the content, and move on.
In reality, the web constantly creates problems.
Pages disappear.
Servers block requests.
Content loads dynamically with JavaScript.
Multiple URLs can point to almost identical pages.
Some websites are designed for humans, not machines.
A crawler is not just downloading pages. It is trying to build a useful representation of the internet.
A bigger index does not automatically create better search
One of the easiest mistakes to make is assuming that more documents means better results.
It does not.
A search engine can index millions of pages and still provide poor answers.
The problem is not finding documents. The problem is deciding which ones are worth showing.
For example, a search for how to configure a Linux server might return:
- Official documentation
- A forum post from someone who solved the exact issue
- A copied tutorial
- An outdated blog post
- An SEO page designed to attract traffic
All of these pages may contain similar keywords.
Only some of them are actually useful.
That is where ranking becomes difficult.
Search ranking requires multiple signals
Keyword matching is still one of the most useful tools in search.
Algorithms like BM25 are extremely effective at finding documents that match a query.
However, keywords alone do not always understand intent.
Someone searching for "best privacy browser" might not want pages that repeat those words the most. They may want comparisons, technical discussions, or opinions from people who have actually used different browsers.
Modern search systems combine many different signals:
- Traditional information retrieval
- Semantic search
- Machine learning reranking
- Document quality signals
- User preferences
Each approach solves a different problem.
There is no single algorithm that understands every type of search.
AI makes search more important, not less
There is a common idea that AI will replace search engines.
The reality is more complicated.
AI systems are very good at transforming information into answers, but they still depend on finding reliable information first.
A model can summarize a document, but it does not automatically know if that document is accurate, outdated, or misleading.
Retrieval is still a fundamental challenge.
As AI systems become more common, the ability to find high-quality information becomes even more important.
Search should give users more control
Traditional search engines usually work in one direction.
The search engine decides how results are ranked, and users accept the output.
But different people have different needs.
A developer looking for documentation wants different results than someone researching a topic. A researcher may prefer academic sources, while someone debugging an issue may prefer forum discussions.
Search does not have to be identical for everyone.
Users should have more control over how results are ranked and what sources they prefer.
Privacy does not mean removing features
Privacy-focused products often face a difficult balance.
Collecting more data can improve personalization, but it also creates questions about how that data is stored and used.
A search engine should be able to provide useful features without requiring users to give up control of their data.
That is one of the challenges I have been exploring while building Slick.
What building search taught me
Building a search engine changed how I think about the internet.
The web looks organized when you browse it normally.
But when you try to index it, the complexity becomes obvious.
Every search result represents many decisions:
- Should this page be indexed?
- Is it relevant?
- Is it trustworthy?
- Is there a better source?
- Should it rank higher?
The search box is the simplest part.
Everything behind it is where the real engineering happens.
Building search is not just about collecting more pages. It is about helping people find useful information in a constantly changing environment.
That problem is still worth solving.
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