Athina AI
Monitor LLMs and automatically detect hallucinations in prod
Athina AI launched on March 7, 2024 with 509 votes, #35 of 4,270 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
Athina AI is a monitoring platform that helps developers track large language models in production environments. It provides visibility into retrieval-augmented generation (RAG) pipelines and includes over 40 preset evaluation metrics designed to automatically detect hallucinations and measure model performance. The tool is intended for teams building and maintaining LLM applications who need to ensure reliability and accuracy in live deployments.
written from the facts on this page · September 2026
From the sources
Athina helps developers monitor and evaluate LLMs in production. Get complete visibility into RAG pipeline and 40+ preset eval metrics to detect hallucinations and measure performance
Athina is a collaborative AI development platform designed for your team to build, test and monitor AI features. Athina enables both non-technical & technical users to collaborate on experiments, evaluating datasets, managing prompts and flows. Compare datasets side-by-side and interact with your datasets in powerful ways using SQL. Humans are able to pick up on nuances that automated evals might not be able to. Athina is designed for human QA teams to work side-by-side with AI evaluations. Everything in Athina works with or without your code. Engineers are able to run prompts, flows, and evaluations programmatically, while non-technical users can use the UI. AI traces have different…from athina.ai
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Open-source model and scorecard for measuring hallucinations in LLMs2023 · vectara.com · ▲65Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
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