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Build AI chat for spreadsheets

GrammarlySoftware Engineer · Onsite
MediumSoftware EngineerOnsiteML System Design

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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.

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.