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LLMValidate.ai
Open evaluation framework for LLMs and RAG pipelines.
LLMValidate.ai evaluates LLM and RAG applications before and after they go live. It uses a self-hosted vLLM judge, local small models such as toxic-bert for safety checks, and custom scoring modules, and it plugs into any RAG pipeline over HTTP. Built by testAIng.com (AiEnsured), an ISO/IEC 42001 certified AI assurance company.
We built LLMValidate.ai to make it easy to evaluate LLM and RAG apps before and after launch, using a self-hosted judge and local safety models, with no vendor lock-in.
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About LLMValidate.ai on Product Hunt
“Open evaluation framework for LLMs and RAG pipelines.”
LLMValidate.ai was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #156 on the daily leaderboard. LLMValidate.ai evaluates LLM and RAG applications before and after they go live. It uses a self-hosted vLLM judge, local small models such as toxic-bert for safety checks, and custom scoring modules, and it plugs into any RAG pipeline over HTTP. Built by testAIng.com (AiEnsured), an ISO/IEC 42001 certified AI assurance company.
LLMValidate.ai was featured in Developer Tools (521.6k followers) and Artificial Intelligence (481.8k followers) on Product Hunt. Together, these topics include over 220.8k products, making this a competitive space to launch in.
Who hunted LLMValidate.ai?
LLMValidate.ai was hunted by Srinivas Padmanabhuni. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Srinivas Padmanabhuni