Craig Roberts

Creating Hinge’s First AI Feature

2024
Product
  • Consumer
  • Social
Platform
  • iOS/Android
Scope
  • Product Strategy
  • Experience Design
  • AI Design
  • Org Leadership
What I did

I owned the problem that AI strategy wasn’t turning into product, enabled live testing, and built real AI systems and internal APIs so I could ship Hinge’s first AI features and create a repeatable process for teams.

Role

Staff Product Designer

Release Date

October 2024

Runtime

7 Months

Cast
  • Cross Functional VPs
  • Cross Functional Discipline Directors
  • Two Product Teams
  • Daters
  • Hinge Engineering Team
User Problem

Dating often leaves people feeling uncertain and inauthentic. They don’t know what to say, how to represent themselves honestly, or how to move forward with confidence at different moments in the journey.

Org Problem

Hinge had no way to place AI directly into these moments of user uncertainty. The team couldn’t test whether AI could actually help people express themselves or feel guided in real dating situations.

Business Problem

AI investment increased without producing shipped experiences that users could feel or trust, creating risk that spend and opportunity were growing while user value remained unproven.

The Finale

Product Impact

First shipped AI feature

System impact

Repeatable AI execution model

Org Impact

Teams gained the tools and process to ship AI features

This work produced Hinge’s first shipped AI feature and established a repeatable execution model. Before it, AI discussions ended in opinion and deferral. After it, they ended in shipment or clear rejection. When the sprint ended without ownership, I carried the work forward and partnered with the Identity team to ship Prompt Feedback, doing the invisible work beyond Figma required to make it real.

Prompt Feedback shipped as Hinge’s first AI feature validated with real users, reducing low-quality prompts by 33%, increasing high-quality responses by 174%, and doubling conversation-to-app download rates. In parallel, I rebuilt the AI vision as an end-to-end working system, ran a second Labs sprint with real daters, and institutionalized demo days, making it the gate every AI feature now passes.

The Prompt Feedback feature appears in a Mashable feature article following its public launch, in New York, NY. After being tested internally six months earlier, the work was productized and brought to market as Hinge’s first AI feature, reducing low-quality prompts by 33%, increasing high-quality responses by 174%, doubling the conversation-to-app download rate, and marking a shift from AI experimentation to shipped, user-facing value.
Concepts from Hinge’s first internal dogfooding session are displayed and tested end to end at the Labs Science Fair in New York, NY. The session reflects a shift from prolonged debate to execution, directly accelerating the shipping of Warm Intros and Prompt Feedback and leading to the formation and funding of a new team focused on reimagining onboarding, establishing decisions grounded in lived experience rather than speculation.

7 Months Earlier

Team

Labs Team (Lead PD, Staff Data Engineer, Lead UXR), Identity Team (PM, DES, UXR, DA)

Location

New York, NY Hinge HQ

Stakes

Whether AI decisions would translate into real product

By the time the AI Vision Sprint launched, AI was no longer theoretical. The decision frameworks I created had aligned the organization on what responsible AI in dating could be, but that alignment introduced a new risk. If the sprint produced only decks and concepts, it would undo the progress that made AI decidable.

With teams pulled from core work and skepticism still high, failure to produce something tangible would retroactively invalidate the work. At the same time, no team at Hinge had any way to evaluate AI in a live product environment.

Employees work inside a glass-walled meeting space at Hinge headquarters in New York. The scene comes as the company enters its AI Vision Sprint, a moment when AI is no longer theoretical but still unproven in live product environments, raising the stakes for turning shared frameworks into something tangible rather than another set of concepts.

The Conflict

Tension

Vision without execution

Constraint

No live AI evaluation medium

Risk

Design theater at company scale

Vision moved faster than execution. There was no live AI evaluation medium, no shared way to test ideas with real users, and no technical path from concept to product. As a result, leadership discussions turned into design theater at company scale, where promising AI ideas accumulated without any system capable of shipping them.

A custom-built AI prototyping system runs alongside live code during early concept work at Hinge headquarters in New York. The setup reflects a period before widely available AI prototyping tools, when building bespoke infrastructure was necessary to make AI concepts tangible enough to evaluate, test, and debate responsibly.
An AI-powered onboarding concept runs on a mobile prototype during the AI Vision Sprint at Hinge headquarters in New York. The work marks the first time real AI systems were used to visualize how dater input could shape onboarding flows and recommendation feeds, moving AI exploration from theory into a live, testable experience.

The Climax

Action

Build live AI systems

Mechanism

Develop OpenAI endpoints + internal Hinge API

Shift

Vision → use

I decided AI work would only matter if it could run end to end in a real product environment. I built live AI infrastructure with OpenAI-backed endpoints and an internal Hinge API, closing the gap between prototype and production.

During the AI Vision Sprint, I shifted the focus from concepts to execution by wiring live AI into design prototypes and ending the sprint with a demo day where ideas were used, not debated. That moment turned Prompt Feedback from speculation into a shipping decision and set the foundation I later carried forward in Labs to build the broader end-to-end AI experience.

A prototype profile built on real dater data runs during the AI Vision Sprint at Hinge headquarters in New York. The internal AI API, developed in partnership with engineering, allows concepts to be tested in live product conditions, marking a shift from speculative decks to experiences the company could evaluate and trust.
The Prompt Feedback feature appears in a live profile-editing flow at Hinge headquarters in New York. After the AI Vision Sprint, I took responsibility for ensuring the ideas shipped, working with the Identity team to deliver the feature by authoring the prompts, designing the interactions, and advocating for animation through successive iterations.
A highlight reel from Hinge Labs aligns teams around an end-to-end AI experience ahead of the Labs Science Fair in New York, NY. After the AI Vision Sprint, I carried the remaining vision into Labs and built it end to end, extending the work beyond the original strategy through co-creation with daters, a redesigned landing experience, the company’s first real voice onboarding, a long-debated WYSIWYG editor, AI-generated summaries and reflections, and “Vibes,” Hinge’s first AI-native dating primitive that shifted dating from browsing profiles to learning about yourself.

Details

Genre

0→1 Product + Platform Creation

Audience
  • Design Leaders
  • Product Leaders
  • Engineering Leaders
  • Organizations facing an inability to execute vision
Tagline

“Vision doesn’t fail. Execution does.”

Team Budget(s)

Est. $2.8–3.4M over 7 months

Est. $12–20M attributable value