Technical practice
Designing Agentic Workflows
What to coverExplain your approach to designing agentic workflows that require multi-step reasoning and tool usage. Discuss how you handle state management, error recovery when an agent fails a step, and the trade-offs between autonomous decision-making and human-in-the-loop oversight.
Evaluating AI Product Performance
What to coverDescribe how you build evaluation frameworks for LLM-based products. Focus on how you measure quality, accuracy, and task completion rates, and suggest how you would differentiate between model performance and overall product utility.
RAG Implementation and Optimization
What to coverDiscuss your experience with Retrieval-Augmented Generation (RAG) in a production environment. Explain how you handle data retrieval accuracy, context window limitations, and the process of iterating on retrieval strategies to improve response relevance.
Responsible AI in Fintech
What to coverHow do you balance the need for rapid AI innovation with the strict requirements of a regulated financial environment? Address how you collaborate with Risk, Compliance, and Operations to implement guardrails and ensure model reliability.
AI Product Prototyping
What to coverDescribe your process for moving from a prototype to a production-ready AI feature. Explain how you determine when a concept is ready for scaling and how you iterate based on experimental data.
Tool Calling and Integration
What to coverExplain the challenges of integrating LLMs with external APIs or internal tools. Discuss how you ensure the model correctly interprets tool documentation and handles unexpected outputs or API failures.
Managing AI Model Drift and Iteration
What to coverHow do you monitor and iterate on AI models once they are in production? Suggest methods for identifying performance degradation and the process for updating prompts or underlying logic without disrupting the user experience.
Defining AI Success Metrics
What to coverBeyond standard engagement metrics, what specific KPIs would you define for an AI money assistant? Explain how you link technical AI performance metrics to business outcomes like customer credit building or loan access.
Behavioural practice
Navigating High Ambiguity
What to coverDescribe a situation where you had to build a product in a highly ambiguous environment with rapidly changing technology. Explain how you prioritized features and maintained team alignment despite the uncertainty.
Collaborating with Technical Teams
What to coverProvide an example of how you have worked closely with engineering and data science teams to solve a complex technical problem. Highlight how you communicated product requirements while respecting technical constraints.
Distinguishing Demos from Solutions
What to coverTell us about a time you had to decide whether to pursue an interesting AI capability or pivot to a more practical customer solution. What was your reasoning, and what was the outcome?
Managing Stakeholder Conflict
What to coverDescribe a time you had to balance the interests of different stakeholders, such as Compliance and Engineering, while launching a new product. How did you reach a consensus that satisfied both regulatory needs and product goals?