AIMon

Check out how you can build more deterministic LLM apps with LlamaIndex, Milvus, and AIMon below. The notebook goes over how to build an LLM chatbot and how to find and fix inaccuracies with AIMon. Looking for a TypeScript version? https://lnkd.in/gjDGsgm7 #llamaindex #milvus #aimon #hallucination #TypeScript #Python

Join Bibek Paudel, an ex-Stanford AI Lab researcher, and Puneet Anand for an interactive conversation on the topic of "Graph RAG" on Feb 26th at 10:00 AM. In this session, we will explore how Graph RAG harnesses the power of graph databases and knowledge graphs to enrich traditional RAG workflows. You’ll learn about the history of knowledge graphs, how they enable more sophisticated data linking, semantic search, and contextual reasoning, as well as the pitfalls and challenges to be aware of.

AIMon's co-founder and CEO Puneet Anand and Founding Applied Research Scientist Bibek Paudel will be hosting a booth and presenting at the sold-out Llama Lounge event tomorrow. If you are attending the event, please stop by and say hello!

Exciting News! We’re thrilled to announce our partnership with ApertureData, the makers of ApertureDB - a leading multimodal database!! ?? Now you can build your RAG App using ApertureDB to store embeddings, use AIMon to monitor context and output quality, and continuously improve your RAG LLM Apps! Check out our new recipe on how to build and improve your RAG LLM apps with ApertureDB and AIMon: https://lnkd.in/g4gtSE7N

We bring you the Prompt and Pizza series by AIMon. Join us for a fun discussion on building LLM apps and improving them incrementally! Registration link: https://lu.ma/dd2yeoag

?? ???Feature Announcement! We are excited to introduce a new feature called "Instruction Adherence" with an F1 Accuracy score of 87.25%. ?? Why it matters: Instructions are commonly used with Large Language Models to define expected behaviors. Deviations from these instructions can negatively impact business impact, safety, and user satisfaction! Monitoring and evaluating for such deviations is critical. ?? How it works: Our detector, powered by a smaller model, has an accuracy (F1 score) of 87.25% with an average latency of 2.7 seconds, based on a derivative of an industry-standard benchmark dataset for the instruction-following task. Whether you’re continuously monitoring your LLM applications, conducting offline evaluations, or setting up guardrails, this tool is designed to keep your AI on track. Check out a quick demo below showing how our adherence detector works on a sample app from our Github repo. ?? Learn more: Dive into the details in our latest blog post: https://lnkd.in/ehV4CYRp ??? If you want to take AIMon out for a spin (for free), here is a sign-up link: https://lnkd.in/gN2ww6B3 ?? If you are curious and want to learn more, check out our sample apps and docs below: https://lnkd.in/ghZU-r7E https://lnkd.in/gmVFWF-H

?? AIMon has been accepted into the NVIDIA Inception Program! AIMon is proud to announce its acceptance into the NVIDIA Inception program. NVIDIA Inception provides a valuable platform to help accelerate AIMon's growth and unlock Generative AI ? innovation with its groundbreaking Reliability product, AIMon Rely. AIMon Rely detects sentence-level hallucinations in under 500ms and 1/10th the cost of GPT-4 and also offers Offline Evaluation and Continuous Monitoring support for other model quality metrics such as Conciseness, Completeness, Toxicity, etc. Keeping flexibility in mind, it can be used through a single configurable API or a complete E2E product. We look forward to a productive collaboration with NVIDIA and continued leadership in shaping the future of LLM adoption. #Nvidia #Aimon #GenerativeAI #AI #Evals #Monitoring #Hallucination #Conciseness #Completeness #AimonRely

?? Hallucination Station! In our new article, we explore real-world stories where AI hallucination caused serious negative business impact. (Link Below) Aimon has developed tech to help you avoid Hallucinations in your LLMs. With our sentence-level Hallucination detection technology, you can use our API to get real-time or offline feedback and take actions such as blocking, user indication, re-prompting, and more. Apart from this, we have a long list of output quality metrics that are available such as conciseness, completeness, toxicity, and more. Reach out to us on Linkedin or at [email protected]. Link: https://lnkd.in/g-ahGDAy #GenerativeAI #AI #MachineLearning #Hallucination

LLMs can suffer from being too verbose in their responses or generate incomplete answers to a user's query. To ensure great customer experience from your LLM applications, it is essential to integrate these quality metrics into your offline evaluations and to consistently monitor them. ?? We are happy to announce version 0.1.0 of "aimon-rely" that includes 2 new response quality metrics: completeness and conciseness. As with our hallucination detection, these detectors incur only 1/10th of the cost of GPT-4 at the same or better accuracy. Read our latest blog post for more details: https://lnkd.in/gHmk8CcE Try out?"aimon-rely" for free (https://lnkd.in/gD7-kKXj)! Request an API key through?Discord (https://lnkd.in/gszY8v23),?WhatsApp (https://lnkd.in/gAbS4tZ7)?or by sending us a note at?[email protected]?

We are thrilled to introduce Aimon Rely, a cost-effective, low-latency solution for detecting hallucinations in LLM outputs. Benchmark evaluations show Aimon Rely performs on par or surpasses GPT-4 Turbo in accuracy. Read our full blog to learn more: https://lnkd.in/gCrPcxa3 Highlights: ?? Costs 1/10th of GPT4-as-judge ? 4x faster ? Suitable for offline evaluations or continuous monitoring ?? Fully hosted API (on-prem as an option) ?? Easy integration with your favorite LLM platform Check out our Github repo (https://lnkd.in/gCAqKWXx) which contains an example Langchain application that demonstrates Rely's ease of integration. For API access, please reach out to us at [email protected] or send us a message on Linkedin. #generativeai #hallucination