Let’s start by questioning whether a chatbot is truly the best solution for the problem you’re tackling.
Simply adding a chatbot into a startup focused on, say, finding trendy pubs, bars, and restaurants doesn’t necessarily make it more better. Most applications won’t gain any real advantages by transitioning to text–based interfaces. This is especially true for tools geared toward data processing, such as management systems or spreadsheets. And while graphical user interfaces excel in many scenarios and text–based ones have their own set of strengths, each type comes with tradeoffs, too.
Determining the optimal conditions for utilizing chatbots is increasingly critical, especially given the current buzz surrounding large language models. And while ChatGPT got extremely popular, it raises the question: is emulating it necessarily the right move for everyone?
ChatGPT presents an interesting paradox: On one hand, it transcends traditional GUIs by allowing users to make open–ended requests instead of being confined to predefined features with buttons. On the other hand, it reintroduces the complexity of command–line interfaces, as users find themselves needing to remember specific incantations—or prompts—to get the results they want.
Enter cognitive ergonomics. It’s the art that focuses on optimizing mental workflows to allow users to comfortably assimilate new information under specific conditions. From this domain, we can adopt the notion of cognitive efficiency—essentially, the fewer steps needed to accomplish a task, the more efficient and comfortable the experience for the user.
For example, if you’re looking to schedule a new meeting in a calendar app, you’d typically need to unlock your phone, open the app, input the meeting’s date and time, and invite attendees. This process could take around a minute. On the other hand, if you’re near an Amazon Echo speaker that’s always on, you could simply tell Alexa to set up a meeting with your friends for after work tomorrow. This reduces the entire procedure to just a matter of seconds.
In scenarios like these, the development of chatbots is entirely warranted. However, if a voice or text interface doesn’t enhance the efficiency of a specific task, its implementation could be called into question. This basic guideline can serve as a useful principle when you’re designing your own products.
A more pragmatic approach might involve hybrid solutions, seamlessly transitioning users between conversational and graphical interfaces based on the requirements of the specific task.
This method was employed at my previous startup, which focused on AI for the real estate industry. We used a chatbot to gather apartment or location criteria from users with text conversations. This allowed buyers to succinctly convey all relevant details in one brief message, avoiding the complexity of configuring multiple advanced filters. However, we displayed search results in a web application where users could also manage their meetings, as this aspect would have been cumbersome to navigate using a text interface, especially for multiple appointments.
If, after employing a similar evaluation approach, your chatbot concept remains viable, you’re likely heading in the right direction—well done! However, it’s important not to adopt specific technologies merely because they’re en vogue. The key is to understand the conditions that make a given solution most effective and to apply that knowledge judiciously. This principle holds true for chatbots as well.
In the product world, features under development are often dubbed WIP: work in progress. In this vibe, I offer you Week–in–Progress: your concise guide to research, insights, and noteworthy stats on emerging companies and projects. This digest goes live every Wednesday on my LinkedIn profile, so don’t forget to subscribe to catch the latest edition tomorrow.
And don't worry: our usual in–depth pieces will continue to roll out on Before Growth, including weekly summaries. WIP is designed to be a quick, under–a–minute read. Some weeks, it simply doesn’t feel right to include it with the essay as I’d like to focus on a single topic—like today.
Case study: Turn rough notes into content with AI
Every new wave of technology seems to bring with it a fresh note-taking app that captures the public’s imagination. In its time, Evernote revolutionized the field with its unique approach to note–taking and information organization. Then came Notion, which won people over with its intuitive design and versatile features.
Most recently, I’ve come across Strut, an AI–powered notebook designed for creators, writers, and teams. Utilizing LLM technology, Strut transforms hastily written notes into polished content through the power of natural language processing.
Two compelling questions emerge when examining this case study.
- First, there’s the matter of Strut’s hybrid interface, which seamlessly blends graphical and chat–based user interfaces. Interestingly, the principles behind this design choice align well with the concepts I’ve discussed today, even though the app didn’t specifically inspire this week’s essay.
- Second, we need to ask whether Strut qualifies as a feature, a product, or, potentially, a full–fledged business. This is a question that virtually every app in this category has grappled with in the past.
For a meaningful analysis, we’ll weigh Strut—and indeed, any emerging note–taking app—against the current market leader when it comes to design: Notion.
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