Data quality assessment is moving beyond static profiling and hard-coded rules. Modern pipelines now use LLMs to infer semantic constraints, detect anomalies in unstructured fields, and generate human-readable quality reports. This guide walks through a practical, code-first approach to building an LLM-powered data quality layer, using standard Python and the OpenAI SDK. Because data quality workloads often involve long schemas, wide CSV samples, and multi-turn reasoning, inference costs can escalate quickly with token-based billing. Oxlo.ai offers a flat per-request alternative that stays predictable as your context grows, and its OpenAI-compatible API means you can run these examples with minimal changes.
Why Use LLMs for Data Quality Assessment
Traditional data quality tools rely on type checks, regex, and fixed thresholds. They catch syntax errors but miss semantic drift, such as a job_title of CEO paired with a department of Engineering, or a country_code that does not match the phone_number prefix. LLMs excel at these cross-column, context-aware validations because they encode world knowledge and reasoning capabilities. They can also generate remediation suggestions and dynamic validation rules from natural language descriptions of your data.
Prerequisites and SDK Setup
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