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Start using the data retrieval platform of the future.
Backed by
Y Combinator
The DeepEval LLM Evaluation Platform.
Built by the creators of DeepEval, companies of all sizes use Confident AI to benchmark, safeguard, and improve LLM applications, with best-in-class metrics and guardrails.
*Learn how Confident AI help Supernormal cut LLM cost by 80%
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LLM Evaluation, Done Right.
Confident AI's core features ensures you do LLM evaluations the proper way to achieve the best possible LLM testing results required for iteration.
Curate Dataset
Annotate datasets on Confident AI and pull it from the cloud for evaluation.
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Run Evaluations
Benchmark LLM systems to experiment with different implementations.
Improve Dataset
Keep your dataset up to date with the latest realistic, production data.
Align Evaluation Metrics
Tailor your LLM metric results to your specific use case/criteria.
Curate Datasets On One, Centralized Platform.
Dump Google Sheets, Notion, or whatever your domain experts are currently using to curate evaluation datasets, and let Confident AI unify your LLM evaluation workflow.
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from deepeval.datasets import EvaluationDataset
from deepeval.metrics import AnswerRelevancy
dataset = EvaluationDataset()
dataset.pull(alias="QA Dataset")
dataset.evaluate(metrics=[AnswerRelevancy()])
Move Fast, Don't Break Things.
Our Pytest integration enables you to unit test LLM systems in CI/CD, compare test results, detect performance drift, without changing the way you work.
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from deepeval import assert_test, LLMTestCase, EvaluationDataset
from deepeval.metrics import AnswerRelevancy
dataset = EvaluationDataset()
dataset.pull(alias="QA Dataset")
@pytest.mark.parametrize("test_case", dataset)
def test_llm_app(test_case: LLMTestCase):
assert_test(test_case, metrics=[AnswerRelevancy()])
> pip install -U deepeval
> deepeval test run test_llm.py
✓ Test Run Completed.
✓ 1/1 test case(s) passing.
Monitor & Trace, To Improve Evaluation Data.
Confident AI will automatically evaluate monitored LLM outputs, and let you decide on which real-world data to include in your dataset for subsequent testing.
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import deepeval
# after your LLM has finished generating
deepeval.monitor(
input="Whatever input your LLM is getting",
response="Whatever your LLM has generated",
model="gpt-4o",
*kwarg
)
Annotate Once, to Align Metrics With Expectations.
Everyone has their own opinions, and Confident AI is here to make sure your evaluation metrics are as aligned with your company's values as possible.
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if needs_improvement(your_llm_app):
you_need_confident = True
else:
you_need_confident = False
Our Evaluation Way Is Proudly Open-Source
I mean, how else could we possibly deliver you the best evaluation results?
300,000+
Daily evaluations
200+
Github stars
100,000+
Monthly downloads
WARNING: You'll Hate Confident If...
You love mysteries and being kept in the dark
Sorry, we don't hide our metrics behind APIs that "beat the SOTA benchmark by 36%".
You love wasting time going around in circles
We help you make sure that iterations only lead to improvements, not regressions.
You have low standards for evaluation metrics
Unfortunately for you, we put a lot of care in tailoring metrics and ongoing alignment.
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You think “collaboration” means sharing a messy CSV file
That's great, but we built Confident for teams, even for non-technical members.
You're OK waiting days for support to "get in touch shortly"
We actually respond. Fast. Our team is here to help, not hide behind chatbots.
You prefer using multiple tools instead of one
Red-teaming, guardrails, observability, anything else we missed on your checklist?
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.
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1.42m
Evaluations completed
Users evaluate by writing and executing test cases in python.
The Future of AI Depends On Confident AI You.