Deep Evaluation for GenAI
Large language models evaluation platform with quality metrics to fit every model and scenario.
We know how to measure the quality of LLMs
Large language models (LLMs) offer valuable insights, aid chatbot management, generate high-quality content such as articles, product descriptions, social media posts, can analyze large volumes of unstructured data or analyze market trends. Yet, conventional evaluation metrics can't tell whether your LLM meets your objectives. Toloka's Deep Evaluation platform bridges this gap, offering an extensive range of tailored quality metrics and pipelines aligned with your specific business context.
Be confident in your LLM's performance with our reliable evaluation framework
Don't let your model give customers false information, or harm revenue with poor fact-checking
Evaluate your model in real- world scenarios and make enhancements to align with your goals and protect your business from risks
Make sure you're adopting the best base LLM for your application
Comprehensive evaluation empowers your team to align language model performance with expectations, ensuring outputs are accurate, reliable, and socially responsible.
Why Toloka LLM Evaluation
Tailored performance metrics
Custom evaluation plan designed for your unique business case with the most extensive range of quality metrics in the market
Scalable human insight
Reliable results from our skilled annotators and domain experts in fields like computer science, EU law, medicine, and more
In-depth evaluation
Our evaluation process harnesses large language models, automated algorithms, and human expertise for deep insights and detailed reports
Business problem decomposition
+
Evaluation metrics
+
Expert labelers
TOLOKA DEEP EVALUATION
We capture the right metrics for your GenAI application
Deep Evaluation in practice
We analyze each model's unique usage scenario and capture the best metrics to measure model performance
How we do evaluation
Analyze your model's performance andusage scenario.
Propose evaluation metrics that fit the unique context and goals of yourmodel.
Create an evaluation pipeline that combines automation and human labeling.
Deliver comprehensive reports with insights on how to improve your model.
