Prototyping in Interviews | Theory & Practice

The Shift to Prototyping in Interviews [00:00:39]

• Companies are moving from presentation-based interviews to requiring live, working prototypes.
• This shift aims to assess candidates' ability to build functional features, not just present ideas.
• The goal is to identify "full stack builders" by making the building process part of the interview.

  "Now we're starting to see that instead of delivering a presentation, companies are asking for a prototype."

The Power of Context Engineering [00:03:24]

• "Context engineering" is crucial for AI tools, providing all necessary information beyond just the prompt.
• This includes functional specs, design details, and technical design documents.
• Applying this to prototyping means giving the AI tool comprehensive inputs for a better outcome.

  "It's really a matter of giving the AI all of the information that it needs to complete the task."

What Interviewers Look For: Taste and Craft [00:04:50]

• Interviewers assess "taste" or "product sense," focusing on attention to detail and user experience.
• They look for candidates who actively drive the tool's output, making deliberate decisions.
• The aim is to distinguish between a prototype built by AI and one crafted by a candidate with intention.

  "There's a really big difference between someone just built a prototype and the AI made all the decisions versus someone actually said, I'm going to work really closely with this tool and I'm going to make the decisions and drive what the end result looks like."

Common Prototyping Mistakes [00:06:11]

• Key mistakes include neglecting copy, which is vital for showing taste and craft.
• Not considering the underlying data and how it surfaces to the user is another common pitfall.
• The goal is to achieve "suspension of disbelief," making the prototype feel like real, working software.

  "Your prototype is not the working software. It's not the production feature, but it should feel like it is."

Crafting a Superior Prototype [00:07:34]

• Prototypes should look cared for and bespoke, avoiding generic AI aesthetics.
• Attention to copy is essential, ensuring it's not awkward or AI-generated.
• Incorporating realistic elements like placeholder images or avoiding them altogether signals a more polished product.

  "It shouldn't look like everything else, right? It shouldn't look like purple gradients on a white background, like a lot of AI prototypes."

Elevating Prototypes with Design and Data [00:11:10]

• A suboptimal prompt focuses solely on functionality, leading to generic results.
• An optimal approach involves providing context across functionality, design (wireframes), and data (JSON).
• This "full stack context" to the AI tool yields a more polished and intentional prototype.

  "So here we're providing design instructions, data instructions, and functionality instructions to the AI."

Integrating APIs for Dynamic Prototypes [00:17:06]

• APIs allow prototypes to fetch live data, moving beyond static mockups.
• Understanding basic API verbs (GET, POST, PUT, DELETE) is helpful, but tools can abstract complexity.
• By pointing the prototyping tool to API documentation, candidates can create dynamic interfaces.

  "Update the weather card to fetch live data from the OpenMedia API, instead of using hard coded values."

Combining APIs for Complex Functionality [00:22:12]

• APIs act like "Lego blocks," enabling the quick creation of complex functionalities.
• Combining multiple APIs (e.g., trivia, weather, image, text generation) can result in sophisticated, interactive prototypes.
• A geography trivia game example demonstrates combining front-end and back-end APIs for a rich user experience.

  "So we have four of these building blocks together, that we're going to combine into something that's pretty cool."