How do you evaluate LLM accuracy?

We construct custom, domain-specific evaluation datasets based on your exact enterprise data. We then utilize LLM-as-a-judge frameworks alongside deterministic metrics (BLEU, ROUGE, BERTScore) to rigorously measure accuracy, tone, and hallucination rates.

Why shouldn't we rely on public benchmarks like MMLU?

Public benchmarks measure general capability across standardized tasks. Enterprise use cases are highly specific. An LLM that scores well on a generic benchmark may still hallucinate frequently when querying your proprietary internal documentation.

Empirical Benchmarking

Moving beyond public benchmarks.

Generic LLM leaderboards do not reflect your proprietary use cases. We engineer bespoke evaluation pipelines to measure hallucination rates, RAG recall accuracy, and semantic alignment using your exact enterprise data.

Discuss Evaluation Criteria

Metrics That Matter

Data-Driven Model Selection

We provide quantitative clarity on which foundational model or fine-tuned configuration truly performs best for your specific application.

Hallucination Measurement

Rigorous detection of factual inconsistencies and confabulations, utilizing advanced LLM-as-a-judge frameworks and deterministic factual grounding checks.

RAG Pipeline Accuracy

Evaluating the retrieval component (hit rate, MRR, nDCG) separately from the synthesis component to pinpoint the exact source of inaccuracies in your architecture.

Cost vs. Quality Optimization

Detailed analysis mapping token consumption and latency against output quality, identifying opportunities to route simpler tasks to cheaper, faster models.

Frequently Asked Questions

We construct custom, domain-specific evaluation datasets based on your exact enterprise data. We then utilize LLM-as-a-judge frameworks (often utilizing Claude 3.5 Sonnet or GPT-4o as impartial adjudicators) alongside deterministic metrics (BLEU, ROUGE, BERTScore) to rigorously measure accuracy, tone, and hallucination rates across thousands of runs.

Public benchmarks measure general capability across standardized tasks. Enterprise use cases are highly specific. An LLM that scores phenomenally well on a generic benchmark may still hallucinate frequently when querying your proprietary internal documentation, or it may struggle to output the exact JSON schema your downstream software requires.

Absolutely. RAG evaluation is a core specialty. We evaluate both the 'Retrieval' component (using metrics like Mean Reciprocal Rank and nDCG to ensure the right documents are fetched) and the 'Generation' component (ensuring the LLM synthesizes those documents accurately without adding external, unverified information).