LLM Evaluation · Testing · Benchmarking

LLM Evaluation
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Q: What LLM evaluation metrics does XPndAI measure?

XPndAI measures: Hallucination Rate (fabricated facts per 100 outputs, measured against ground-truth reference), Factual Accuracy (correct answers on domain benchmark), Instruction Following Rate (compliance with system prompt constraints), Safety Score (harmful, toxic, or non-compliant outputs per 100), Coherence and Fluency (logical consistency of multi-turn responses), Groundedness (for RAG: answer supported by retrieved context), Calibration (does the model's expressed confidence match its actual accuracy), Latency P50/P95/P99, and custom domain metrics you define. Each metric is scored with statistical confidence intervals so you know whether a model change is a genuine improvement or noise.

You've fine-tuned an LLM or built on top of one. Before it goes to production, you need to know its hallucination rate, domain accuracy, safety posture, and how it compares against the baseline. We measure all of it — with human evaluators and automated benchmarks.

Hallucination Rate Factual Accuracy Safety Evaluation Human Evaluation Model Comparison Benchmark Datasets

Get LLM Evaluated All Metrics

LLM Evaluation — Free Scoping

Tell us your LLM use case and what you're worried about. We'll scope the evaluation in 30 minutes.

Evaluation Metrics

What We Measure in Every LLM Evaluation

🧪 Hallucination Rate

Fabricated facts per 100 outputs. Measured against ground-truth reference corpus specific to your domain.

✅ Factual Accuracy

% correct answers on domain-specific benchmark. Separate scores for easy, medium, and hard questions.

🛡️ Safety Score

Harmful, toxic, biased, or non-compliant outputs per 100 inputs. Jailbreak resistance testing included.

📋 Instruction Following

Compliance rate with system prompt constraints, output format requirements, and persona instructions.

🔗 Coherence

Logical consistency across multi-turn conversations. Does the model contradict itself? Lose context?

🎯 Calibration

Does model confidence match actual accuracy? A well-calibrated model is uncertain when it should be.

⏱️ Latency P95

95th percentile time-to-first-token and total response time under realistic production load.

🔄 Regression Score

Performance comparison against baseline version. Catches silent degradation from prompt or model changes.

Evaluation Services

LLM Evaluation Engagements

🔬

Hallucination Audit

Purpose-built for teams worried about factual accuracy in high-stakes domains — medical, legal, financial, or real estate AI. We build a ground-truth reference corpus from your source documents and measure exactly how often the LLM fabricates.

Custom ground-truth corpus from your docs
Hallucination rate with confidence intervals
Hallucination type breakdown (entity, date, number, relationship)
Fix recommendations (prompt, RAG, fine-tune)

⚖️

Safety + Compliance Evaluation

For regulated domains — BFSI, healthcare, legal. Tests whether the LLM refuses prohibited requests correctly, produces compliant outputs, and handles adversarial inputs without leaking unsafe content.

Jailbreak resistance testing (100+ attack patterns)
Harmful content rate measurement
Regulatory compliance check (RBI, SEBI, HIPAA, GDPR)
PII leakage detection in outputs

🏆

Model Comparison

When choosing between Claude, GPT-4, Gemini, and fine-tuned alternatives — or comparing prompt versions — we run both through your domain benchmark and give you evidence-based selection, not benchmarks written by the model vendors.

Head-to-head on your specific task type
Domain benchmark (not generic MMLU)
Cost per correct answer comparison
Latency vs accuracy tradeoff analysis

👤

Human Evaluation

Automated metrics miss what domain experts catch. We source evaluators from your industry — CAs for finance AI, real estate agents for property AI, doctors for medical AI — and get structured human ratings on outputs automated scoring can't capture.

Domain-expert evaluator sourcing
Structured rubric per use case
Inter-rater reliability measurement
Qualitative failure analysis

🔧

Fine-Tune Evaluation

Before and after fine-tuning — did it actually improve? Did it degrade performance on tasks not in the fine-tune set? We run the full evaluation suite on base vs fine-tuned model so you have evidence the fine-tune was worth it.

Base vs fine-tuned comparison
Capability regression detection
Overfitting indicators
Recommendation: further fine-tune, prompt, or RAG?

📊

Continuous Production Eval

Production LLMs drift. User inputs evolve. New failure modes emerge. We sample production conversations, evaluate them against your benchmark, and alert when quality drops below acceptable thresholds.

Weekly/monthly production sampling
Quality trend dashboard
Regression alert when score drops
Production failures → new benchmark cases

Evaluation Cluster

Full AI Evaluation Stack

AI Agent EvaluationFull agent QA → RAG EvaluationRetrieval + faithfulness → Voice AI TestingSpeech + conversation → AI Agent DevelopmentWe build it too → Document AIExtraction + accuracy → Enterprise AIMulti-agent →

Pricing

LLM Evaluation Pricing

From targeted hallucination audit to full evaluation infrastructure. Fixed scope, clear report deliverables.

Targeted Audit

₹1.5L – ₹5L

One evaluation focus area

1 evaluation type (hallucination / safety / accuracy)
100-case benchmark dataset
Automated + 1 human evaluation round
Findings report with fix recommendations
1–2 week delivery

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Common Questions

What is LLM evaluation and why does it matter?

LLM evaluation is the systematic measurement of a large language model's performance across accuracy, reliability, safety, and domain-specific correctness. It matters because LLMs hallucinate (generate confident-sounding false information), drift when updated, and behave differently with adversarial inputs vs curated demos. Without evaluation you don't know your hallucination rate, you can't detect regressions, and you can't prove to enterprise customers or regulators that the model was validated before deployment.

What LLM evaluation metrics does XPndAI measure?

Hallucination Rate (fabricated facts per 100 outputs against ground truth), Factual Accuracy (correct answers on domain benchmark with easy/medium/hard split), Instruction Following Rate (system prompt compliance), Safety Score (harmful or non-compliant outputs per 100), Coherence (multi-turn logical consistency), Calibration (confidence vs actual accuracy), Latency P50/P95, and Regression Score vs previous version. Custom domain metrics are defined in the scoping call.

Can you evaluate any LLM — including closed-source models like GPT-4 and Claude?

Yes. We evaluate any LLM accessible via API — GPT-4, Claude, Gemini, Llama, Mistral, your own fine-tuned model, or any model hosted on your own infrastructure. For closed-source models we evaluate via their API exactly as your production system would call them. For fine-tuned or custom models we need API access or a hosted endpoint. We do not require access to model weights for standard evaluation.

LLM EvaluationServices