Task-Specific Evaluation: Code, SQL, and JSON Correctness
If you are fine-tuning a model to write code, generate SQL, or extract structured data, you have access to a luxury most evaluation methods ignore: verifiable ground truth. While BERTScore and LLM judges try to approximate quality, task-specific checkers provide objective, binary, and cost-effective signals.
What you’ll learn in this technical guide:
Code Correctness: How to use the unbiased pass@k estimator and why you must run generated code in a sandboxed, isolated subprocess to prevent security regressions.
Text-to-SQL Accuracy: Why comparing raw query text fails due to syntactic freedom, and how to use execution accuracy—comparing result sets—as the gold standard for correctness.
Structured Data (JSON) Extraction: How to go beyond whole-object matching by using schema validation and field-level precision, recall, and F1 scoring.
The Checker Pattern: A universal four-step framework to build deterministic checkers for any domain, ensuring you always distinguish between "couldn't even attempt" failures and "attempted but wrong" failures.
Common Pitfalls: How to unit-test your checker, avoid data leakage, and handle non-determinism in SQL/floating-point comparisons.
Stop relying on subjective similarity scores for tasks that have a objectively correct answer.
#LLM #FineTuning #AIEngineering #MachineLearning #ModelEvaluation #CodeGeneration #TextToSQL #DataExtraction #ArtificialIntelligence #TechTutorial
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