Domain-Specific Evaluation

Generic evaluation metrics miss domain-specific quality. Code generation needs execution tests. Summarization needs faithfulness checks. Classification needs confusion matrices. This article builds custom evaluators for the most common AI application domains, with production-ready code for each.

Quick Reference

→Code generation: pass@k (probability of at least one correct solution in k samples) is the standard metric
→Summarization: evaluate faithfulness (no hallucination), coverage (key points included), and conciseness separately
→Classification: precision/recall/F1 per class, macro vs micro averaging, confusion matrix analysis
→Information extraction: exact match, fuzzy match, and field-level metrics for structured output
→Build domain evaluators as composable functions that return standardized score dictionaries

Code Generation Evaluation

Code is unique among AI outputs: it can be objectively verified by execution. Does it compile? Does it pass tests? Does it produce the correct output? This makes code generation evaluation more rigorous than most domains — but execution-based evaluation has its own challenges, including security (running untrusted code), environment setup, and flaky tests.

Never execute untrusted code without sandboxing

LLM-generated code can contain anything — file system operations, network calls, infinite loops. Always execute in a sandboxed environment (Docker container, E2B, Modal sandbox) with resource limits (CPU time, memory, network access).

Code generation evaluation with execution-based testing and pass@k

Summarization Evaluation

Summarization evaluation requires three orthogonal dimensions: faithfulness (does the summary only contain information from the source?), coverage (does the summary include all key points?), and conciseness (is the summary appropriately brief?). Optimizing all three simultaneously is the challenge — a longer summary improves coverage but hurts conciseness.

Classification Evaluation

When LLMs are used for classification (sentiment, intent, topic), standard ML metrics apply directly. But there are LLM-specific considerations: the model may refuse to classify, produce an invalid label, or hedge with multiple labels. Your evaluation must account for these failure modes.

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