Building AI Products Taught Me That LLMs Aren't the Hard Part

When I started building AI products, I thought the hardest part would be integrating an LLM.

It wasn't.

Calling an API takes minutes.

Building a product that people can actually trust takes months.

Like many developers, my first prototype worked surprisingly fast. I connected an LLM, wrote a few prompts, and got impressive responses.

For a moment, it felt like most of the work was already done.

It wasn't even close.

The real problems started afterwards

The biggest challenge wasn't generating answers.

It was making those answers reliable.

Some responses were excellent.

Some were inconsistent.

Some confidently described things that weren't actually there.

If you're building an AI product, that inconsistency isn't just a technical issue.

It's a product problem.

Reliability matters more than intelligence

Users don't judge AI by its smartest answer.

They judge it by its worst one.

One unreliable response can destroy confidence much faster than ten good ones can build it.

That completely changed how I think about AI products.

Instead of asking:

"How can I make the model smarter?"

I started asking:

"How can I make the system more trustworthy?"

Those are very different problems.

Building around the model

Over time I realized that the LLM is only one small part of the product.

Most of the engineering effort happens around it.

Things like:

• validating inputs

• collecting structured evidence

• retry strategies

• timeout handling

• consistent formatting

• reducing hallucinations

• cost optimization

• making outputs actionable instead of just impressive

None of these appear in a flashy demo.

But they're what make an AI product usable.

AI is a product, not a prompt

One lesson surprised me the most.

Good prompts are helpful.

Good systems are essential.

The quality users experience comes from the entire pipeline—not just the model.

That's why building AI products feels much closer to software engineering than prompt engineering.

What I'm building

These lessons eventually became the foundation of the product I'm building today.

Instead of treating AI like a chatbot, I'm using it to evaluate digital products before release and transform findings into structured, actionable insights.

The goal isn't to replace designers, QA engineers, or developers.

It's to help product teams make release decisions with more confidence.

I'm still learning every day, but one thing has become clear:

The LLM was never the hardest part.

Building something people can trust is.

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I'd love to hear from other builders.

What has been the hardest part of building AI products for you?