Stop Designing AI Gimmicks in Products

There’s been a wave of AI features in the market that exist just to slap Artificial intelligence onto a product — without actually improving user experience. You’ve probably seen them: flashy, automated tools that generate half-baked results, leaving you either stuck with meh output or spending extra time fixing what should have been helpful in the first place.

Think of those AI-powered email generators that sound robotic, or “smart” document summarisation tools that strip out the actual meaning. Instead of making life easier, they create more work. AI should be a force multiplier, not a fancy gimmick. The difference? Giving users control over the AI — so they can shape the output instead of just accepting whatever comes out.

Here’s how AI features should actually be designed to be useful.

Let users fix AI midway, instead of cleaning up after it

One of the biggest mistakes in AI product design is assuming that plugging an API into your product will work the same magical way as ChatGPT does in a chatbox. Spoiler: it won’t. Many teams have been wowed by how generative AI responds in consumer apps, assuming that integrating it into their own product will instantly deliver the same smooth, intelligent results. But raw APIs don’t come pre-packaged with context, user adaptation, or fine-tuned interaction design.

The result? AI generates responses that are almost right — but just off enough that users have to spend extra time fixing them. And right now, most AI tools don’t let users step in before things go sideways. Instead, they’re stuck either accepting a flawed response or rewriting everything from scratch.

A smarter approach is to let users guide AI while it’s generating — not just after the fact.

AI generates text token by token — if it makes a mistake early, that mistake snowballs. Midway corrections prevent bad output from getting worse.
Think of it like a GPS for AI — if the model starts going in the wrong direction, users should be able to reroute before it’s too late.
Saves time on editing — why force users to clean up an entire response when they could have nudged AI toward the right answer from the start?

Right now, too many AI features take a “fire and forget” approach — one click, full output, take it or leave it. But the best AI products will be the ones that let users steer AI in real-time, ensuring that what comes out is actually useful before it’s fully generated.

Stop the one-size-fits-all approach: let users define AI’s personality

A robotics engineer, a pharmaceutical researcher, and a mobile app developer all need AI — but they don’t need the same AI. Yet too many AI products assume that a single response style, structure, and level of detail will work for everyone. That’s like handing every professional the same generic toolset and expecting them to build completely different things.

If your product serves a diverse user base, AI must adapt to the industry — not the other way around.

In robotics, an AI assistant might need to generate technical documentation with structured schematics and step-by-step assembly instructions. A casual summary won’t cut it.
In pharmaceuticals, researchers need AI to produce highly detailed reports with precise citations from scientific literature — not generic, blog-style overviews.
In mobile app development, engineers might want AI to generate clean, modular code with inline comments and best practices, rather than just dumping raw code snippets.

The fix? Let users define the AI’s behaviour, structure, and depth. Instead of forcing a rigid, one-size-fits-all response style, allow configurable system prompts tailored to each industry’s needs.

A robotics company could set AI to always include regulatory compliance notes in technical reports.
A pharma team might configure AI to prioritise peer-reviewed sources and clinical trial data.
A developer could ensure AI follows their coding guidelines and uses preferred frameworks.

The best AI products won’t just generate answers — they’ll generate answers in the right format, structure, and level of detail for each user’s domain. The companies that get this right will dominate their industries, while the ones forcing generic AI onto specialised users will be left behind.

Generic AI knowledge won’t cut it — let users bring their own data

Even the best LLMs have knowledge gaps. AI can’t be useful if it doesn’t know the specifics of your work. The worst AI implementations rely entirely on static, general knowledge — leading to vague or irrelevant responses.

The fix? Let users feed their own documents and data into the AI.

This is called Retrieval-Augmented Generation (RAG), and it’s what actually makes AI intelligent in real-world applications. Instead of relying purely on pre-trained knowledge, AI can reference:

Internal company policies and training manuals
Research papers, PDFs, and technical documents
Product databases and FAQs

Think about customer support chatbots. The ones that only pull from generic AI knowledge often give useless, generic responses. But a bot that can reference a company’s internal documentation? Now that’s useful.

RAG is the difference between AI guessing and AI actually knowing what it’s talking about.

Stop building AI for hype — build it for users

oo many AI features today exist just to say, “Look! We have AI!” — but they don’t actually improve anything. The result? A flood of gimmicky, frustrating, and ultimately useless tools that don’t fit the needs of real users across different industries.

The smarter way to build AI? Make it adaptable. Make it user-driven.

✅ Let users correct AI midway, so they can steer responses in real time instead of fixing bad output later.
✅ Allow industry-specific system prompts, so a robotics engineer, a pharmaceutical researcher, and a software developer all get AI that thinks like they do.
✅ Enable domain-specific data uploads, so AI isn’t just generating from generic knowledge — it’s working with the exact information users rely on.

AI that actually helps people isn’t about slapping automation onto a product — it’s about giving users control over how AI thinks, responds, and adapts to their needs. Stop making users adjust to AI’s limitations. Instead, let them shape it to fit their world.

The future of AI isn’t in flashy features — it’s in putting real power where it belongs: in the hands of the user.

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