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Airtable·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026Remote

Summary

Airtable ran what they called an 'AI Fluency' coding round, which basically meant building a small integration pipeline under interview conditions. The task was more product-engineering than pure algorithms, which I wasn't fully expecting.

Questions Asked (1)

Q1

Build a flow that takes a natural-language instruction from a user, uses an LLM to convert it into a valid Airtable formula, and applies that formula to a live sheet via the Airtable REST API. You're given both an Airtable API key and a Claude API key. Handle auth, API errors, and prompt design.

API & IntegrationsSystem DesignTechnical Trade-offs
Author's notes

The scope of this caught me a bit flat-footed.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a modular pipeline: input validation, LLM prompt with schema context, formula validation, and API execution with error handling. Emphasize security, reliability, and iterative refinement based on API feedback.

Pro tip: Always validate the LLM-generated formula against Airtable's formula syntax and test it on a small sample before applying to the live sheet; this prevents costly errors and demonstrates production-ready thinking.

1. Clarify Requirements and Constraints

Ask about the expected input format, error tolerance, and whether the formula should be applied to all records or a subset. Confirm the Airtable base/table schema and permissions.

2. Design the LLM Prompt

Craft a prompt that includes the user's instruction, the Airtable table schema (field names and types), and examples of valid formulas. Instruct the LLM to output only the formula and handle ambiguity.

3. Validate and Sanitize the Formula

Check the generated formula for syntax errors and injection risks. Optionally, use a dry-run endpoint or a test table to verify it works before applying to the live sheet.

4. Execute via Airtable API with Error Handling

Use the Airtable REST API to apply the formula (e.g., via a filterByFormula parameter or update records). Implement retries with exponential backoff for transient errors and log failures.

5. Monitor and Iterate

Provide feedback to the user and log outcomes. If errors occur, refine the prompt or add guardrails, and consider caching successful formulas for similar instructions.

Key Points to Mention

  • Authentication: securely store and use API keys (e.g., environment variables, secret managers) and handle token expiration.
  • Prompt engineering: include schema context, few-shot examples, and constraints to improve LLM accuracy.
  • Error handling: distinguish between LLM errors (e.g., invalid output) and API errors (e.g., rate limits, 4xx/5xx) and implement appropriate retries and fallbacks.
  • Security: sanitize LLM output to prevent formula injection and ensure it adheres to Airtable's syntax.
  • Testing: validate formulas on a small dataset or sandbox before applying to production data.
  • Scalability: consider rate limits, batch operations, and asynchronous processing for large tables.

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