Automate LLM app evaluation to accelerate deployment and ensure quality
Deepchecks helps AI teams test and monitor their large language model applications by automating the evaluation process. It allows you to set up auto-scoring pipelines and compare different versions without manual effort, so you can release high-quality apps faster. This reduces risks like hallucinations and speeds up innovation for organizations.
Automatically score LLM responses for accuracy and relevance
Compare prompts, models, and agents to choose the best version
Test and monitor LLM apps within continuous integration pipelines
Use a swarm of small language models for precise assessments
Create no-code evaluators using LLMs and chain-of-thought
It addresses the complexity of evaluating LLM applications by providing standardized metrics, auto-scoring, and streamlined version comparisons to reduce risks and speed up deployment.
It offers superior accuracy for groundedness and retrieval relevance, backed by studies, using a swarm of small language models and multi-step NLP pipelines.
Yes, it can evaluate anything from simple RAG systems to complex agentic workflows using evaluation agents that assess different spans.
This tool is best for:
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Deepchecks can be integrated with this app or service:
Recent data shows that Deepchecks has 109.0K monthly visits (-7.1% decrease from the previous month), 44.0% bounce rate, and average 1.62 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 5 countries worldwide, led by India contributing 10% of total traffic. SEO performance is shown by 5 tracked keywords, with "deepchecks" being the top-performing keyword with 520 monthly searches. See below for more info.
Monthly Visits
109.0K
Pages per Visit
1.62
Bounce Rate
44.0%
Average Time on Site
32s
| SEO Keyword | Volume | CPC |
|---|---|---|
| deepchecks | 520 | $4.30 |
| precision recall sensitivity specificity accuracy auc f1 | 220 | - |
| rmsprop | 4.3K | - |
| comprobar algoritmos programa | 200 | - |
| top kaggle real world dataset financial | 150 | - |
| Source | Traffic Share |
|---|---|
| Direct | 36% |
| Search | 52% |
| Social | 3% |
| Referrals | 8% |
| Paid Referrals | 1% |
| Country Name | Traffic Share |
|---|---|
 | 10% |
 | 7% |
 | 4% |
 | 4% |
 | 3% |
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