HumanSignal for Model Evaluation

Uniquely customize the degree of automation and human supervision to evaluate and take control of your GenAI applications.

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Evaluation Aligned To Your Needs

HumanSignal provides a flexible approach to evaluation, allowing organizations to choose the level of automation based on their specific needs and confidence requirements.

Fully Automated Evaluation
Automate the evaluation process using other LLMs as judges. While this approach offers speed and efficiency, it may not match the precision of manual reviews.
Hybrid Evaluation
Combine manual and automated evaluations to balance accuracy and efficiency. Use automation for initial checks and deploy expert reviews for more complex or critical assessments.
Fully Manual Evaluation
For the highest accuracy, leverage internal experts to manually review and validate LLM outputs. Ideal for critical tasks where precision is paramount, despite the higher cost and time investment.

Trust Requires Human Signal

Generative AI is powerful, but hallucinations and bias often make it risky to deploy in mission-critical applications. While we support fully-automated evaluation, for applications requiring a high degree of trust and safety, enabling human supervision is recommended for increased:

Contextual Accuracy
Enhanced Relevance
Continuous Improvement

Ensure your models are accurate, aligned, and unbiased.

Ready-to-use Evaluators

Get the precision and relevance your projects need.

Select from a range of pre-built evaluators, including PII and toxicity, to start assessing your models instantly.
Craft your own custom metrics and fine-tuned evaluators for specialized, domain-specific applications.

Comprehensive Dashboards

Gain crystal-clear insights into your model's performance with our advanced evaluation dashboards.

Leverage the power of LLM-powered judges alongside human evaluations for a comprehensive performance analysis. Switch between different LLM backends or adjust prompts to compare multiple judges, identify overlaps, and optimize for efficiency and cost.
Contrast LLM-as-a-judge outputs with manual evaluations, or delve into specific disagreements for deeper context. Make data-driven decisions with clarity and precision.

Integrated Human Workflows

Automatically generate predictions in a labeling project for data visualization and human review. The reviewed data can then be fed back into your model for additional evaluation, including:

Side-by-side comparison of two model outputs, or model outputs against ground truth data
RAG pipeline evaluation using ranker and LangChain
LLM response moderation & grading

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Get expert advice and help implementing a proof of concept based on your unique use cases.