Accuracy is the vanity metric of LLM evaluation. A model can be factually correct while being incoherent, stylistically inconsistent, or unsafe—rendering it useless for production applications. This guide covers the essential quality dimensions that separate prototype demos from production-ready systems.
Why Traditional Accuracy Metrics Fail in Production
Most teams start their LLM evaluation journey by measuring accuracy on benchmark datasets. While useful for model selection, accuracy alone creates dangerous blind spots:
The Accuracy Trap: A customer support chatbot might correctly answer 90% of questions but deliver responses in fragmented, robotic language that frustrates users. Your accuracy metrics look great, but user satisfaction plummets.
Real-World Impact: One enterprise deployment of a document summarization system achieved 88% factual accuracy but scored below 40% on coherence metrics. The result: 60% user rejection rate and complete system redesign after three weeks.
Production quality requires holistic measurement across multiple dimensions that reflect actual user experience and business outcomes.
Coherence measures how well an LLM’s output hangs together as a unified, logical whole. It’s not just about correct facts—it’s about the relationships between those facts.
Key Coherence Sub-metrics:
Global Structure: Does the output follow a logical progression? (Introduction → Body → Conclusion)
Local Connectivity: Do sentences and paragraphs flow naturally? Are transitions smooth?
Referential Integrity: Are pronouns, definite descriptions, and cross-references used correctly?
Thematic Consistency: Does the output maintain focus on the core topic?
Production LLM systems fail when they optimize for the wrong metrics. Accuracy without coherence creates responses that are factually correct but unusable. Consider these real-world failure patterns:
The Documentation Disaster: A code documentation generator achieved 92% accuracy on technical facts but scored 0.32/1.0 on coherence. Developers rejected 73% of generated docs because the explanations jumped between topics without logical flow.
The Compliance Nightmare: A legal document review system correctly identified 89% of compliance issues but failed safety checks 22% of the time by generating biased language. The organization faced regulatory scrutiny despite “high accuracy” scores.
The Brand Damage: A marketing content generator produced factually accurate product descriptions with inconsistent tone across 500+ assets. The rebranding cost exceeded $2M when the inconsistency was discovered post-launch.
These failures share a common root: teams measured accuracy while ignoring the quality dimensions that drive user trust, compliance, and brand integrity.
"passed": all(v >= 0.7 for v in scores.values() if isinstance(v, float))
})
# Aggregate
agg = {}
for metric in self.metrics:
agg[metric] = sum(r["scores"].get(metric, 0) for r in results) / len(results)
return {
"individual_results": results,
"aggregate_scores": agg,
"pass_rate": sum(r["passed"] for r in results) / len(results)
}
# Usage example
evaluator = QualityEvaluator(openai.Client())
test_cases = [
{
"id": "test_001",
"prompt": "Explain quantum computing",
"response": "Quantum computing uses qubits. These are different from classical bits. Qubits can be 0 and 1 simultaneously. This enables exponential speedup."
OpenAI Evals: Framework for creating and running evaluation benchmarks for LLMs. Includes pre-built evals for common tasks and custom criteria. github.com/openai/evals
RAGAS: Specialized framework for evaluating Retrieval-Augmented Generation pipelines with metrics for faithfulness, answer relevance, and context precision. github.com/explodinggradients/ragas
G-Eval: Paper introducing chain-of-thought evaluation for summarization quality with state-of-the-art correlation to human judgments. arxiv.org/abs/2303.16634
LLM-Rubric: Multidimensional calibrated evaluation framework using rubric-based scoring. arxiv.org/abs/2501.00274
A Survey on LLM-as-a-Judge: Comprehensive review of evaluation methodologies, biases, and best practices. arxiv.org/abs/2411.15594
Alternative Evaluation Paradigms
Judge-Free Benchmark: Distribution-based evaluation without human or LLM judges. arxiv.org/abs/2502.09316
This guide provides the foundation for production-quality LLM evaluation. Start with the four-pillar framework, implement the code example, and iterate based on your specific use case requirements.
