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The LLM Evaluation Platform.
Companies of all sizes use Confident AI to benchmark, unit test, and red team LLM applications - may it be LLM Chatbots, RAG, or Agents.
TRUSTED BY TOP COMPANIES AROUND THE WORLD
Automatically Catch Regressions in LLM Systems.
Unit test LLM systems, compare test results, detect performance drift, optimize on prompt templates, and identify the root cause of regressions.
Custom evaluation metrics for any use case.
Evaluate on any criteria using research-backed LLM-as-a-judge metrics, proven to be as accurate and reliable as human evaluation.
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4.28m
Evaluations completed
Battle-tested with over 4 million evaluations ran.
40+
Metrics available
For LLM safety, RAG, agents, or chatbots, etc.
Tailored synthetic dataset generation for every customer.
Generate test cases that makes sense for your use case on the cloud to manage evaluation datasets on one centralized platform.
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100k+
Test Cases Generated
Custom to your style & data, using our expertise.
31+
Hours Saved Per Week
Automated annotation, dataset versioning, and so much more.
Automated LLM red teaming to detect safety risks.
Discovery which combination of hyperparameters such as LLMs and prompt templates works best for your LLM app.
2.4x
Less time to production
No more time wasted on finding breaking changes.
1.42m
Evaluations completed
Users evaluate by writing and executing test cases in python.
Powered by DeepEval and integrates with any LLM system.
Run evaluations and monitor LLMs on the cloud through simple APIs via DeepEval, Confident AI's open-source LLM evaluation framework.
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from deepeval import confident_evaluate
test_case = LLMTestCase(input="...", actual_output="...")
confident_evaluate(experiment_name="RAG Test", test_cases=[test_case])
> pip install -U deepeval
> deepeval test run test_llm.py
✓ Test Run Completed.
✓ 1/1 test case(s) passing.
Judge your LLM application on one, centralized platform.
Deploy LLM solutions with confidence, ensuring substantial benefits and address any weaknesses in your LLM implementation.
Define ground truths to ensure your LLM is behaving as expected
Supply ground truths as benchmarks to evaluate your LLM outputs. Evaluate performance against expected outputs to pinpoint areas for iterations.
Advanced diff tracking to iterate towards the optimal LLM stack
From altering prompt templates to selecting the right knowledge bases – we guide you towards the optimal configurations for your specific use case.
LLM observability and monitoring to identify areas of focus
Utilize out-of-the-box observability to identify and evaluate use cases that bring the most ROI for your enterprise.
Powerful features to productionize LLMs with confidence.
A/B testing
Compare and choose the best LLM workflow to maximize your enterprise ROI.
Evaluation
Quantify and benchmark your LLM outputs against expected ground truths.
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Output classification
Discover recurring queries and responses to optimize for specific use cases.
Reporting dashboard
Utilize report insights to trim LLM costs and latency over time.
Dataset generation
Automatically generate expected queries and responses for evaluation.
Detailed monitoring
Identify bottlenecks in your LLM workflows for targeted iteration and improvement.
Book a demo today.
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2. Integrate with your tools
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The future of evaluation depends on you.
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Support team
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