Build AI chat for spreadsheets
Company: Grammarly
Role: Software Engineer
Category: ML System Design
Interview Round: Onsite
Design an AI chat interface for the spreadsheet product.
A user can type natural-language commands such as:
- "Sum all values in column K and write the result to cell C,Y."
- "Delete row K."
- "Delete column J."
- "Insert a new row after row M with values [...]."
The assistant must translate the user request into structured spreadsheet operations through a controlled tool-calling layer similar to an MCP-style tool registry. The system should validate permissions and parameters before execution, apply the changes safely, and return a human-readable response.
Discuss:
- the LLM inference/orchestration pipeline,
- prompt and tool schema design,
- API design and important parameters,
- authorization and validation,
- ambiguity handling and confirmation flows,
- rollback or atomic execution for multi-step commands,
- prompt injection and safety risks,
- latency, cost, scaling, and observability.
Quick Answer: This question evaluates a candidate's ability to design ML-driven conversational interfaces and systems integration, emphasizing LLM orchestration, tool schema and API design, authorization and validation, ambiguity handling, atomic multi-step execution, safety against prompt injection, and operational concerns like latency, cost, scaling, and observability. It is commonly asked to assess architectural thinking for integrating language models with controlled tool-calling and safe execution; category: ML System Design; level of abstraction: system-level design requiring both conceptual understanding and practical application.