Model Evaluation
Expert Data for LLM Testing
Our comprehensive evaluations use expert knowledge to align model performance with your expectations, ensuring AI outputs are accurate, reliable, and aligned with your requirements.
Start evaluation
The Evaluation Gap
Automated Metrics Do Not Tell the Full Story
Standard evaluation metrics - accuracy, perplexity, BLEU scores - provide incomplete pictures of model performance. They miss nuance, fail to capture user experience, and often do not correlate with real-world utility. Models can score well on benchmarks while producing outputs users find unhelpful or frustrating.
Without human evaluation, teams make decisions based on incomplete data. Model improvements that boost automated scores may degrade user experience. Deployment decisions lack the qualitative insight needed to predict real-world performance.
Human-in-the-loop evaluation provides the qualitative depth automated metrics lack. Expert assessors evaluate model outputs against real-world criteria, providing nuanced feedback that guides meaningful improvements and informs deployment readiness.
Incomplete Metrics
Automated scores capture narrow aspects of performance while missing critical dimensions like helpfulness, coherence, and factual accuracy.
Benchmark Limitations
Public benchmarks often do not reflect real use cases. Models optimized for benchmarks may fail on tasks users actually care about.
Subjective Quality
User satisfaction depends on subjective factors that automated metrics cannot measure.
Why Rise Data Labs
Structured Evaluation by Trained Assessors
Our evaluation framework combines rigorous methodology with trained human judgment. Assessors follow structured rubrics, provide consistent ratings, and deliver actionable feedback, giving you confidence in the evaluation results and clarity on next steps.
01
Structured Rubrics
Clear criteria for consistent evaluation.
- Dimensions Explicit, task-specific criteria
- Scoring Comparable across runs and versions
- Stability Reproducible results over time
02
Trained Assessors
Judgment informed by context.
- Expertise Evaluators trained on your model and use case
- Context Decisions grounded in real deployment scenarios
- Calibration Aligned to your quality standards
03
Actionable Insights
Feedback you can act on.
- Examples Concrete success and failure cases
- Patterns Systematic issues surfaced
- Next Steps Clear recommendations for iteration
Evaluation Types
Comprehensive Model Assessment
Type
Description
Use Case
Output Quality Assessment
Human ratings of model outputs on dimensions like accuracy, helpfulness, coherence, and safety.
Identify quality issues, track improvements, and compare model versions.
Comparative Evaluation
Side-by-side comparison of multiple models or model versions on identical inputs.
Model selection, A/B testing, and competitive benchmarking.
Task-Specific Testing
Evaluation on custom task suites designed to reflect your actual use cases and user scenarios.
Validate performance on real-world tasks not covered by standard benchmarks.
Error Analysis
Systematic categorization and analysis of model failures to identify patterns and improvement opportunities.
Prioritize training data collection and target model improvements.
How It Works
The Evaluation Process
01
Evaluation Design
We define evaluation criteria, test sets, and rating scales aligned with your model's purpose and success metrics.
02
Assessor Training
Evaluators are trained on your model, guidelines, and quality standards to ensure consistent, informed assessment.
03
Structured Evaluation
Model outputs are evaluated following defined protocols with inter-rater reliability monitoring and quality checks.
04
Analysis & Reporting
Results are analyzed for patterns, benchmarked against baselines, and compiled into actionable reports with improvement recommendations.
Ready to Evaluate Your Model?
Discuss your evaluation needs with our team. We will design an assessment protocol tailored to your model, use case, and decision criteria.
Request evaluation