AI & Technical question
Pick one concrete international-payments workflow where an LLM could create real leverage at Ramp, for example, KYB document review, payment exception handling, or explaining why a cross-border transfer was delayed. How would you design the human-in-the-loop experience, identify failure modes such as hallucinated compliance conclusions or incorrect document extraction, and decide whether the system is safe and useful enough to ship?
- Ramp
- AI & Technical
- Hard
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What this question tests
Tests AI product design for a high stakes financial workflow, focusing on human in the loop design and identifying concrete failure modes before shipping.
How to approach it
- Pick KYB document review specifically, since it is a well scoped, high volume task with clear ground truth to validate accuracy against.
- Design the human in the loop experience so the LLM extracts and flags fields with a confidence score, and a human reviewer confirms or corrects before any compliance conclusion is finalized.
- Identify failure modes explicitly: hallucinated compliance conclusions, incorrect document field extraction, and missed red flags in unusual document formats.
- Mitigate hallucination risk by never letting the model state a compliance conclusion directly, only surfacing extracted facts and flags for a human to judge against policy.
- Set a confidence threshold below which cases always route to a senior human reviewer rather than the standard review queue.
- Decide the system is safe enough to ship only after validating extraction accuracy against a held out set of real documents with known correct answers, reviewed by compliance.
What a strong answer includes
- Draws a hard line that the model never issues a compliance conclusion itself, only surfaces extracted evidence, which directly addresses the hallucinated conclusion risk named in the prompt.
- Names specific failure modes concretely, hallucinated conclusions and incorrect extraction, rather than a generic reference to AI risk.
- Requires validation against a held out set with known correct answers before shipping, giving a concrete, falsifiable safety bar.
Common mistakes
- Letting the model state compliance conclusions directly instead of only surfacing extracted facts for human judgment.
- Shipping based on a general sense of accuracy without validating against a held out set of real documents with known correct answers.
Likely follow-up questions
- How would you set the confidence threshold that routes a case to a senior reviewer?
- What would you do if the model performs well on standard documents but poorly on unusual formats?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture