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Airtable AI Fluency — Interview Talking Points

Opening explanation​

I will separate planning from execution. Claude interprets the user's language and proposes a structured Airtable formula. My application retrieves the authoritative schema, validates Claude's response, checks API capabilities, previews the change, and only executes an approved and supported operation.

The four competencies​

1. Delegation​

  • Delegate semantic interpretation and formula generation to Claude.
  • Keep schema retrieval, field validation, permissions, capability detection, and mutations in deterministic code.
  • Never ask Claude to guess whether a field exists or whether an API endpoint is supported.

2. Description​

  • Give Claude the actual field names and types.
  • Include examples that distinguish + 2, * 2, * 1.2, and * 0.2.
  • Require strict JSON rather than free-form prose.
  • Tell Claude to ask for clarification instead of inventing a field.

3. Discernment​

Validate the response at four levels:

  1. Structural: valid JSON matching the expected schema.
  2. Grounding: every referenced Airtable field exists.
  3. Semantic: the formula represents the user's intended operation.
  4. Operational: the supplied API can actually perform the mutation.

A valid formula does not prove that the API can create the field.

4. Diligence​

  • Keep API keys in environment variables.
  • Send only the minimum schema context to Claude.
  • Preview schema changes and require approval.
  • Never claim success before checking the Airtable response.
  • If formula-field creation is unsupported, return a truthful manual fallback.
  • Do not use eval() to execute model-generated formulas.

Example request​

User:

add two on salary

Preferred interpretation:

{
  "intent": "create_computed_field",
  "fieldName": "Salary Plus Two",
  "formula": "{Salary} + 2",
  "referencedFields": ["Salary"],
  "needsClarification": false,
  "clarificationQuestion": null,
  "explanation": "Adds two to each record's Salary value."
}

What to say about the Airtable limitation​

I have generated and validated the formula, but I will independently verify whether the provided Airtable environment supports creating a formula field. If the operation is unsupported, I will not fabricate a successful API call. I will return the field name and formula for manual creation, or discuss an explicitly approved materialization fallback. Materialized values are not equivalent because they do not recalculate automatically.

Strong narration while coding​

First I am fetching the real Airtable schema so Claude cannot invent fields.

I am using Claude as a planner, not as an authorization or execution layer.

I require structured JSON so the result is machine-verifiable.

Now I am validating the referenced fields and their types.

Next I am checking tool capability separately from formula correctness.

I will preview the action before performing a schema mutation.

I will only report success after verifying the Airtable API response.

Test cases​

RequestExpected formula or behavior
add two to salary\{Salary\} + 2
double salary\{Salary\} * 2
increase salary by 20%\{Salary\} * 1.2
show the 20% salary increase\{Salary\} * 0.2
add 10% to compensation with multiple compensation fieldsAsk for clarification
Request references Base Pay, but only Salary existsReject or clarify; do not invent \{Base Pay\}
Claude returns \{Salary\} * 2 for add twoReject as semantically incorrect
Formula is valid but formula-field creation is unsupportedReturn manual action; report dataChanged: false

Thirty-second closing summary​

My design demonstrates the four Ds. Delegation defines the boundary between semantic AI work and deterministic application logic. Description grounds Claude with the real schema and a strict contract. Discernment validates structure, field references, types, meaning, and API feasibility. Diligence protects credentials, requires approval, handles limitations honestly, and verifies the final result. The objective is not merely to get Claude to output a formula; it is to build a trustworthy workflow around that output.