How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a none difficulty ML System Design question, commonly asked during Onsite rounds at Grammarly.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Grammarly during technical interviews.

Build AI chat for spreadsheets | Grammarly Interview Question

Quick Overview

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.

Build AI chat for spreadsheets

Quick Overview

Solution

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