Design NL-to-Formula assistant for Airtable
Company: Airtable
Role: Software Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
## Scenario
You are given:
- An **Airtable API key** and a link/base/table you can read/write.
- An **LLM API key** (e.g., Claude) that you can call.
Users type natural-language requests such as:
- “add 2 to salary”
- “if status is 'active', set bonus to 10% of salary”
- “create a column that is salary * 1.1”
Your task is to build a service that:
1. Interprets the user request and the table schema.
2. Converts the request into an **Airtable formula expression** (or equivalent transformation) that can be applied to a field/column.
3. Applies the transformation to the online sheet via the Airtable API.
4. Returns a clear result to the user (what was changed, preview, and any errors).
## Requirements
- Must handle ambiguous requests (ask clarifying questions when needed).
- Must be safe: prevent destructive/unsafe operations, data leakage, and prompt injection.
- Must be reliable: validate formulas before applying, handle API errors/rate limits, and support retries.
- Should be observable: logs/metrics/traces for debugging and evaluation.
## Deliverables
- High-level architecture (components and data flow).
- Prompting/tool-calling strategy to translate natural language to an Airtable formula.
- Validation and safety checks before writing back to Airtable.
- How you would test and evaluate quality (offline + online).
- Key edge cases and failure modes.
Quick Answer: This question evaluates a candidate's ability to design an ML-enabled service that translates natural-language spreadsheet commands into executable formula expressions, testing skills in LLM orchestration, API integration, schema inference, ambiguity resolution, validation, safety controls, and observability.