How We Use (and Don't Use) AI in Development

AI is very limited.

Not in the “it’s useless” way. In the “it can produce confident output that still needs a professional” way. Most of what people call “AI” today is an LLM (large language model like ChatGPT or Grok). They are good at generating convincing text and plausible code. But “convincing” is not the same as correct, and “plausible” is not the same as right for your business.

In high-quality custom software, websites, web apps, and mobile apps - AI cannot replace experienced people, and it does not remove steps in development. What it can do is help us explore options, validate decisions, and improve quality when used with discipline.

This article explains how we use AI at LINK-V, and what can realistically end up in your project.

What AI is good at

LLMs are pattern-based tools. They work by predicting what comes next in a text, based on a large amount of training data. That makes them useful for:

• presenting variations
• explaining concepts in different ways
• drafting text from notes
• suggesting code patterns and edge cases
• spotting inconsistencies in wording or structure

They are not good at:

• understanding your full context (business goals, constraints, history, priorities)
• guaranteeing correctness
• making responsible decisions
• building coherent systems over time without strong guidance
• knowing what is true, only what is likely to sound right

That last point matters the most. AI can be confidently wrong. So our rule is simple: AI can assist, but it never gets the final say.

The core principle: expert people stay responsible

In our work, every deliverable has an owner. A real person. AI can support that person, but it cannot replace responsibility.

That means:

• we treat AI output as a draft, not a decision
• we verify by reasoning, review, and testing
• we keep quality standards the same, regardless of whether AI was used

So yes, AI-generated parts can end up in your project. But not unreviewed, not untested, and never blindly trusted.

How we use AI in code

AI can be helpful in programming, but not in the way many AI marketing claims suggest.

The hard parts of software development are often not “typing code”. The hard parts are:

• choosing the right approach (architecture, data flow, performance strategy)
• handling edge cases and real-world behavior
• integrating with existing systems
• maintaining clarity for future development
• preventing bugs, security issues, and regressions

AI helps most with generating options and getting started. It can suggest multiple implementations, remind us of common pitfalls (not project-specific ones), or propose a cleaner structure.

What we do in practice:

• AI-generated code can be used in client projects and products.
• If AI wrote it, it’s labeled by the AI or the programmer.
• A developer reviews any generated code line by line.
• We verify behavior with tests and real usage.

If the code is small and obvious, verification is quick. If it is critical (security, payments, permissions, data integrity), we treat it like any other critical code: deeper review, more tests, and stricter requirements.

How we use AI in graphics, design, and UX

Design is not only about aesthetics. It is communication, clarity, hierarchy, trust, and conversion. AI does not understand your brand and your users the way an experienced designer does.

AI can help generate directions that work for generic audiences (because are well known and are well known), but it doesn’t know your niche customers. That’s still on us.

Where AI helps us:

• brainstorming layout and style directions
• generating alternative or demo phrasing for UI texts
• quick utility tasks like background removal
• evaluation and second opinions (for example, checking whether a flow has confusing steps)

This is mainly about speed and breadth. AI is useful for exploring more variants, so we can choose the best direction or combine themes out of more options.

How we use AI for text and communication

We use AI for:

• first drafts based on bullet points, structure and notes
• rewriting tone (more formal, more friendly, more direct)
• grammar and clarity improvements
• catching awkward phrasing
• cultural and social sensitivity checks (useful for international audiences)

Then we edit the texts. We keep what matches our voice, remove what feels generic, and make sure every claim fits reality. AI can produce text quickly, but it does not guarantee accuracy or relevance.

How we use AI for brainstorming and staying current

AI is useful as a “first or second pass” tool for:

• naming options
• feature lists and trade-offs
• possible risks and edge cases
• alternative approaches to a problem

We also use it to speed up orientation in tech and business news, mainly to get an overview and identify what is worth checking in depth.

But we still verify important information from primary sources. AI can miss options, mix details, or present guesses as facts.

Does AI make the project cheaper or faster?

Sometimes it speeds up small parts. It can reduce the blank-page phase, help with repetitive drafting, and make exploring technical options faster.

AI mostly helps in a less technical way. It’s not a systemic tool that replaces our process or decisions. It’s a personal productivity tool - it helps people get over the blank page, break a problem into smaller parts, and start moving when they are stuck. The value is usually not “AI did the work”, but “it helped the expert do the work better”.

At the same time, it often increases the number of options worth considering, and it always adds the need for verification.

AI does not remove steps from professional development. If used carelessly, it can add new problems that cost time later and reduce reliability.

So the practical answer is:

AI helps us improve quality and efficiency in small ways. It does not replace professional work. It does not turn custom software into a one-click product. AI rarely reduces the scope of work, but it can speed up “connecting tissue” between development steps, which sometimes shortens delivery.

Summary

“AI-powered” has become a vague marketing label at best and unrealistic promises at worst. Thanks to that, some companies use it as “we paste requirements into a tool and ship whatever comes out”. That can be acceptable for very simple, low-stakes pages or prototypes. It is not acceptable for serious business software.

We tested AI tools and unwilling to compromise on quality of the output, we prefer a straightforward approach:

• use AI where it genuinely helps
• keep responsibility with experts
• verify everything that matters

We use AI as a practical assistant to explore options and improve output. But every final decision, every line of code, and every deliverable in our Timeless services and Grace products is owned and verified by our team.