The setting
is a transportation platform that connects riders with rideshare, bikes, scooters and other ways to get around. gives the drivers who provide those rides tools for earnings, payments, safety and support.
The support agent asks for evidence its tools already hold.
Avoiding repeat uploads must preserve requests for photos that are genuinely missing.
Who you’d be doing this for
“I uploaded the same three photos twice, then it asked again while I was cleaning the car.”
Kadiatou Nyarko · Rideshare driver · a character in this brief
Submits a damage claim after discovering a mess following a trip.
What is at stake
Drivers received duplicate photo requests in 24 of 200 damage-claim conversations. You weigh a prompt change that uses receipt state against a backend gate that prevents repeat requests.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- fewer repeat requests vs complete evidence
- model context vs deterministic enforcement
- clear acknowledgment vs implied approval
Why Lyft
LangGraph carries driver damage claims through evidence collection before they reach a resolution.
Written with these in mind
Not your kind of problem? 76 more at Lyft, or browse every organization.
Where the organization context comes fromThe postings this brief was written from, and the reporting read before it.
The organization context comes from public information. The situation and characters are written for the brief.
Job postings it was written from
Public descriptions of the work at Lyft. They are listed as sources, not as openings. The lines under a posting are its own words, where it meets this brief.
- Machine Learning Engineer, Safety and Customer Care AI (opens in a new tab)
- We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection.
- Design and build AI-powered support agents and end-to-end agents for safety case management using LangGraph or equivalent agentic frameworks.
- Data Scientist - Algorithms, Mapping (opens in a new tab)
- Senior Software Engineer, Forecasting & Real-Time Optimization (opens in a new tab)
- The ideal candidate enjoys solving problems with distributed computing for ML and forecasting models, and finding patterns and insights in real-world data while building reliable systems, and is excited about working in a fast-paced, innovative, and collegial environment.
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.
Where similar work appears
Other live briefs connected to this one, and what connects them.
The same setting, from another role
Each is set around Lyft’s Lyft for Drivers, written for a different role. They’re separate briefs, not the same scenario.
Airport wait-time guidance is staying stale after airport communication changes, sending drivers into queues that no longer match the app.
You must decide what the system can rank automatically and what engineers must verify before acting.
Drivers are opening completed trips and still contacting support to ask what changed their pay.
You have to weigh a useful explanation against the stability and confidentiality of earnings configurations.
Safety notifications are flagging ordinary trips after an app release changed incoming sensor signals.
You must weigh immediate protection for drivers against losing coverage for genuinely unsafe driving.