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At some point in their career, every designer has heard the request to make an element "pop more". The premise is simple: to draw attention to a particular element in a layout, make it stand out. However, in practice, the opposite effect often occurs.

Consider the overlaid promotions, tips, or call-outs typically found in an interface. To get these elements noticed, designers will apply contrasting colors, shapes, fonts, and more. All in an attempt to make things pop. But if a pop-up or any element of a user interface for that matter looks too different from the rest of the design, people will often perceive it as something that doesn't belong (like an ad) and dismiss it.

"
We see overlay avoidance consistently in the research we conduct with mobile users. Especially when people are task focussed. Then when they do come to use a new function they can't find the help and don't recall seeing the help previously."
-Lisa Duddington 
Co-founder, Keep It Usable

As Lisa points out, when people are task-focused, they ignore and quickly dismiss overlays assuming they aren’t there to support their core flow but instead to distract from it. Some of these dismissals may even be accidental. That is, people just get rid of dialog-like things by instinct and without thinking.

"We saw this all the time at Facebook. People very often, instinctively, dismiss tooltips. On occasion even accidentally. By the time they realize the message may have been helpful, it's already gone and there's no way to recover it."
-
Tanner Christensen 
Product Designer, Facebook

In cases where the overall actually had useful information, when its gone so is the useful information. And there's usually no way to get it back. Probably not the intended effect of making an overlay more noticeable through design.

On the other hand, if the pop-up looks like an integrated part of the app or website, it's much more likely to be considered and perceived as relevant. In Red Laser’s case, making the primary action button huge and red led to users completely ignoring it. In subsequent redesigns, they toned down the shape and added a label, making it feel more like part of the core navigation. They even dropped the different color.

"We made our ‘scan’ button huge and a different color from the rest of the nav menu and users entirely ignored it."
-Miles Skorpen, Head of Business Development, Red Laser

As these examples illustrate, instead of making elements visually distinct to be noticed, designers should focus on integrating important elements seamlessly into the user interface. This ensures that crucial actions and information are perceived as part of the overall user experience, rather than distractions to be dismissed.

As the number of highly capable large language models (LLMs) released continues to quickly increase, I added the ability to test new models when they become available in the Ask Luke conversational interface on this site.

For context there's a number of places in the Ask Luke pipeline that make use of AI models to transform, clean, embed, retrieve, generate content and more. I put together a short video that explains how this pipeline is constructed and why if you're interested.

Specifically for the content generation step, once the right content is found, ranked, and assembled into a set of instructions, I can select which large language model to send these instructions to. Every model gets the same instructions unless they can support a larger context window. In which case they might get more ranked results than a model with a smaller context size.

Despite the consistent instructions, switching LLMs can have a very big impact on answer generation. I'll leave you to guess which of these two answers is powered by OpenAI's GPT-4 and which one comes from Antrhopic's new (this week) Claude 3.5 Sonnet.

Some of you might astutely point out that the instruction set could be altered in specific ways when changing models. Recently, we've found the most advanced LLMs to be more interchangeable than before. But there's still differences in how they generate content as you can clearly see in the example above. Which one is best though... could soon be a matter of personal preference.

Thanks to Yangguang Li and Sam for the dev help on this feature.

As the functionality and scope of Web sites and applications has grown over the years, so has the prevalence of Help pages. Nearly every feature has an explanatory article outlining how to use it and why. But most Help pages are walls of text making them hard to act on. So a few years ago, we tried something different.

First let's look at the status quo. This Help page from Amazon is both pretty typical and by those standards, pretty good. It's specific to one topic, brief, outlines steps clearly, and includes links to help people accomplish their intended task. Companies iterated to these kinds of Help pages because they mostly work and because they're less work.

Keeping Help text up to date and accurate is less labor-intensive than updating images or videos with the same information. But as the old saying goes, a picture is worth a lot of words and there's a reason many people turn to video tutorials to learn how to do things instead of reading about how to do them.

When building Polar several years ago, we wanted a more approachable and fun way of helping people learn how to use our product. And while you might say "the best Help pages are no Help pages -just make your app easy to use" not all Help pages are smearing over usability issues. Some introduce higher level concepts, others outline capabilities, and some serve as marketing for specific features.

So with those goals in mind, we iterated to a simple formula. Each concept or feature gets a Help page that has a title alongside 1-2 sentences and as many sections consisting of a title, 1-2 sentences, plus a graphic as needed.

This approach meant people primarily relied on images (or their alt tags if visually impaired) to figure out how to get things done. So we iterated a fair amount on the images to find the right balance of detail and abstraction. Make the UI too realistic and it becomes hard to focus on the relevant elements. Realistic UI images also need updating anytime the actual product UI changes. Conversely, make the image too simplistic and it doesn't provide enough detail for people to actually learn how to do things.

Of course, not all Help topics are well suited to an image but the process of trying to create one often triggers ideas on how to simplify the actual UI or concepts within a product. So it's worth the iteration.

But is a visual approach to Help pages able to scale? Assuming it works, can companies invest the time and effort needed to generate all these images and keep them up to date? Perhaps in a time of image generation AI models, it's increasingly possible through automated or supervised pipelines. Time will tell!

Since launching the Ask Luke feature on this site last year, we've added the ability for the system to respond to questions about product design by citing articles, videos, audio, and PDFs. Now we're introducing the ability to cite the thousands of images I've created over the years and reference them directly in answers.

Significant improvements in AI vision models have given us the ability to quickly and easily describe visual content. I recently outlined how we used this capability to index the content of PDF pages in more depth making individual PDF pages a much better source of content in the Ask Luke corpus.

We applied the same process and pipeline to the thousands of images I've created for articles and presentations over the years. Essentially, each image on my Website gets parsed by a vision model and we add the resulting text-based description to the set of content we can use to answer people's design questions. Here's an example of the kinds of descriptions we're creating. As you can see, the descriptions can get pretty detailed when needed.

If someone asks a question where an image is a key part of the answer, our replies not only return streaming text and citations but inline images as well. In this question asking about Amazon's design changes over the years, multiple images are included directly in the response.

Not only are images displayed where relevant, the answer refers to them and often refers to the contents of the image. In the same Amazon navigation example, the answer refers to the green and white color scheme of the image in addition to its contents.

Now that we've got citations and images steaming inline in Ask Luke responses, perhaps adding inline videos and audio files queued to relevant timestamps might be next? We're already integrating those in the conversational UI so why not... AI is a hell of a drug.

Further Reading

Additional articles about what I've tried and learned by rethinking the design and development of my Website using large-scale AI models.

• New Ways into Web Content: rethinking how to design software with AI
• Integrated Audio Experiences & Memory: enabling specific content experiences
• Expanding Conversational User Interfaces: extending chat user interfaces
• Integrated Video Experiences: adding video experiences to conversational UI
• Integrated PDF Experiences: unique considerations when adding PDF experiences
• Dynamic Preview Cards: improving how generated answers are shared
• Text Generation Differences: testing the impact of AI new models
• PDF Parsing with Vision Models: using AI vision models to extract PDF contents
• Streaming Citations: citing relevant articles, videos, PDFs, etc. in real-time
• Streaming Inline Images: indexing & displaying relevant images in answers

Acknowledgments

Big thanks to Sidharth Lakshmanan and Sam Breed for the development help.

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London ; Philadelphia : Jessica Kingsley Publishers, 2024.

[Cambridge, England] : Ethics International Press Limited, 2024.

Abingdon, Oxon ; New York, NY : Routledge , 2024.

Lynnfield, Massachusetts CAST [2024]

Baltimore : Johns Hopkins University Press, [2024]

Toronto : Canadian Scholars, 2024.

Bloomington, IN : Solution Tree Press, [2024]

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