recruiting · workflow
Instacart rethinks SQL interview to test AI-forward data science workflows with LLMs
Traditional SQL interviews requiring candidates to write code manually became ineffective once LLMs could solve the same questions with a simple prompt, making the format a poor test of actual on-the-job skills and unfair to candidates who do not write SQL daily.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Schemas and questions as prompt
The user provides database schemas and business questions as context in a prompt to the LLM.
Tools used
AvaOpenAIGPT-4oSnowflake ArcticLlama 3–70B
Outcome
Instacart redesigned its SQL interview to test AI-forward skills — prompt engineering for SQL, debugging and explaining LLM-generated SQL, and identifying query optimizations — better reflecting how data scientists now work.
Results
Time savedsaving significant amounts of time and effort
Grounding & classification
Source type: technical build writeup
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code generationfailure mode describedhuman review describednamed customersource backedtools describedworkflow describedecommerceemployee productivitytime savedtechnical build writeuprecruitingai draft human approval