Designer Insights: AI Summarization for Better Information Retrieval
In a recent project I’m working on, there’s a classic AI feature involved: using Large Language Models (LLMs) to quickly summarize the main content of multiple articles, helping users efficiently obtain information and make subsequent decisions. In today’s fast-paced world, we’re easily swamped by an overload of information and rarely have enough time to read everything that interests us. Text summarization and information retrieval are precisely where LLMs shine, and at Ekohe, we have ample experience and a stellar team to back us up. Whether it’s designing related products or utilizing them as daily tools, hopefully some of my thoughts from a design perspective could spark some new findings for you.
Overview & Application
AI summarization, the automatic generation of concise content summaries using AI technology, involves summarizing existing content rather than creating entirely new content, which doesn’t quite fall under Generative AI. Whether used for quickly browsing news, research reports, or keeping up with meeting minutes, smart summarization significantly boosts information processing efficiency. Its applications extend beyond text to include media like videos and audio, providing comprehensive summarization services through automatic transcription and content analysis. Introducing smart summarization not only saves users valuable time but also supports more efficient decision-making and knowledge acquisition in an era of information overload.
As a low-risk mode, AI summarization can be pre-introduced in addition to providing some preset or freely input prompts. A common example: Loom automatically transcribes and segments video content after recording and provides a summary. Without affecting the core functionality (video recording and sharing in this case), users often embrace these auxiliary features, especially when they come as a pleasant surprise. Even with some flaws, users tend to be more forgiving of minor errors.
Speaking of which, I recently read one of Nielsen’s articles, “AI: First New UI Paradigm in 60 Years” which mentions that AI is introducing the third user interface paradigm in computing history. This new interaction mechanism has users telling the computer what they want rather than how to do it — fundamentally reversing the control source. These AI summaries, whether presented by default or generated by user-selected or input prompts, theoretically still involve users telling the computer what they want via prompts. By repeatedly adjusting prompts, we can get closer to the precise results we expect. But indeed, when the intermediate process becomes a black box, users have very limited control over the results. How to precisely express, correctly use prompts, and properly guide users in using them may give rise to the profession of prompt engineer. However, I agree with Nielsen’s view that this profession won’t last long — making AI usable by everyone with better usability will still be the long-term trend.
Potential Challenges & Solutions
Using AI to summarize large amounts of text can indeed improve efficiency, but it also has some potential downsides. Even though AI has made significant progress in language understanding in recent years, it still struggles with complex semantics in specific cultural or contextual contexts. There may also be biases or misunderstandings due to algorithm limitations or training data biases, affecting users’ ability to gain a comprehensive and diverse perspective or missing crucial background information. Especially when users don’t know how something is done, it becomes harder to identify or correct problems. These are all factors that we need to take into consideration while designing.
Therefore, when designing such a feature, we often bring in Retrieval-Augmented Generation (RAG) technology to have AI provide inline citations to its sources. These citations help users trace back to the original articles, delve deeper into a topic, or verify if the quoted material is relevant and valid. This way, users won’t entirely relinquish control over content accuracy to the AI search engine.
Another more profound negative impact is that long-term reliance on AI-generated summaries may weaken our own reading and comprehension skills, particularly for handling long texts or complex content. If you frequently use such auxiliary tools, please do remember that AI summaries make it easier to access important information, but they can not replace the information itself — traditional learning and comprehension involve deep reading, critical thinking, and synthesizing information from multiple sources, which develop cognitive skills and a thorough understanding and retention of the material. Relying solely on AI summaries prevents those mechanisms from happening, as users may miss out on the nuances and context provided by the full text — this also underscores the importance of preserving links to the original text to engage with the content more deeply.
Beyond technical and cognitive challenges, AI-generated summaries also raise significant ethical concerns, especially in critical industries like healthcare, human resources, finance, etc. For example, in healthcare, they might omit crucial patient information, leading to misdiagnoses — be more mindful when designing with products in those fields. This introduces another layer of complexity, AI might generate summaries based on incomplete or biased data. While RAG can help by providing verification sources, it is not a complete solution. And talking about data sources, I have noticed that more websites are being protected from scraping recently, which directly leads to skewed results in AI search applications like Perplexity. Ensuring ethical AI use requires oversight and accountability, with user-centric designers often acting as the “last line of defence” on software development teams. Ultimately, we hope to highlight the need to adhere to ethical guidelines on our teams and push for stringent review processes to prevent possible ethical pitfalls from over-reliance on AI summaries. This includes addressing issues related to data accessibility and accuracy, and ensuring that the AI’s sources are comprehensive and representative.
Evolving Role of Design in AI Features
In designing AI-related features, personally I feel that visible, “seen” designs are becoming rarely required. The third AI user interface paradigm is simple and highly convergent, but the considerations behind each design decision have increased. Although the second UI paradigm may not dominate the future any more, it will continue to exist (“clicking or tapping on-screen content remains an intuitive and important way of user interaction”). Still using our project feature as a simple example, questions like: Should we let users click to get summaries? Should we show the AI model used? Should we collect subsequent user feedback (such as thumbs up/down)? Should we support saving outputs temporarily or permanently? Etc, those are still what we need to concern before making decisions. As designers, we serve as bridges — we strive to provide users with sufficient information to build trust while optimizing performance and avoiding cognitive overload. At Ekohe, we integrate user-centric design principles and are involved throughout the development process. We advocate for transparent explanations of AI recommendations to foster user trust and our close collaboration with engineers & data scientists ensures our AI solutions are user-friendly and reliable.
We are fortunate to be at and witness the intersection of user interface paradigm shifts in computing history. This is undoubtedly an exciting yet somewhat anxiety-inducing change. I firmly believe that the way to counter anxiety is to adapt to development, embrace change, and maintain thoughtful consideration — they will be our most powerful tools as designers and our greatest confidence in ensuring user experience.
