Airtable·Software Engineer·Onsite - System Design / Architecture·Senior

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Jun 2026

Summary

Airtable system design round for a software engineer role, focused entirely on one big open-ended problem: build an ML-powered natural-language-to-formula assistant on top of their own product. The question had a lot of surface area and I felt like I was triaging which parts to go deep on the whole time.

Questions Asked (1)

Design an ML-powered assistant for a no-code table product like Airtable that takes a user's natural-language request and converts it into a formula expression grounded in the live table's schema. You have access to an Airtable API key and an LLM API key. Walk through the end-to-end architecture, formula generation strategy, output contract, schema grounding, ambiguity handling, validation, how you apply changes to the live sheet, safety concerns, latency/reliability, evaluation, and edge cases.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This is basically a full system design in one prompt.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and constraints, then walk through a layered architecture: schema extraction, LLM-based formula generation with grounding, validation, and safe application. Emphasize the importance of a robust output contract and iterative refinement to handle ambiguity and edge cases.

Pro tip: Treat the LLM as a code generator that must be constrained by a strict schema and validated against the live table; always include a dry-run or preview step before applying changes to avoid destructive errors.

1. Clarify Requirements and Constraints

Ask clarifying questions about user expectations, supported formula complexity, and safety requirements. Define success metrics and constraints like latency, accuracy, and API rate limits.

2. Design End-to-End Architecture

Outline components: schema fetcher (via Airtable API), prompt builder, LLM caller, formula parser/validator, and applier. Describe data flow and error handling at each stage.

3. Formula Generation and Grounding Strategy

Explain how to ground the LLM with the table schema (field names, types, relationships) and provide examples. Use few-shot prompting and constrain output to a JSON contract with formula and explanation.

4. Validation, Safety, and Application

Detail validation steps: syntax check, type inference, dry-run on sample rows, and user confirmation. Discuss safety measures like permission checks, rate limiting, and rollback plans.

5. Evaluation, Edge Cases, and Iteration

Propose evaluation metrics (accuracy, latency, user satisfaction) and a test suite covering edge cases (ambiguous requests, missing fields, complex logic). Describe how to iterate based on feedback.

Key Points to Mention

Schema grounding: dynamically fetch table schema and inject into prompt to ensure field names and types are correct.
Output contract: define a strict JSON schema for the LLM response including formula, explanation, and confidence score.
Ambiguity handling: detect ambiguous requests and ask clarifying questions or provide multiple options with explanations.
Validation: parse and type-check the formula, run it on sample data, and compare results to expected behavior.
Safety: implement permission checks, rate limiting, and a preview mode before applying changes to the live sheet.
Latency and reliability: use caching for schema, async processing, and fallback mechanisms for LLM failures.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.