Agentic Workflow // Chinar Movsisyan // MLOps Podcast
Reliable LLM Products, Fueled by Feedback // MLOps Podcast #251 with Chinar Movsisyan, CEO of Feedback Intelligence.
Chinar Movsisyan is a forward-thinking software developer specializing in artificial intelligence, notably in the nuanced field of agent workflows and root cause analysis. Chinar's work primarily involves enhancing and orchestrating complex agent workflows, where she has developed innovative approaches utilizing Large Language Models (LLMs) alongside classical methods. Recognized for her technical expertise and detailed-oriented approach, Chinar delves deeply into systems to understand and tackle layered challenges, enabling her to design solutions that address both immediate issues and broader systematic problems. Her commitment to refining these processes is driven by her belief in the need for comprehensive, orchestrated solutions to fully resolve intricate software challenges.
// Abstract
We live in a world driven by large language models (LLMs) and generative AI, but ensuring they are ready for real-world deployment is crucial. Despite the availability of numerous evaluation tools, many LLM products still struggle to make it to production.
We propose a new perspective on how LLM products should be measured, evaluated, and improved. A product is only as good as the user's experience and expectations, and we aim to enhance LLM products to meet these standards reliably.
Our approach creates a new category that automates the need for separate evaluation, observability, monitoring, and experimentation tools. By starting with the user experience and working backward to the model, we provide a comprehensive view of how the product is actually used, rather than how it is intended to be used. This user-centric aka feedback-centric approach is the key to every successful product.
// Bio
Chinar Movsisyan is the founder and CEO of Feedback Intelligence, an MLOps company based in San Francisco that enables enterprises to make sure that LLM-based products are reliable and that the output is aligned with end-user expectations. With over eight years of experience in deep learning, spanning from research labs to venture-backed startups, Chinar has led AI projects in mission-critical applications such as healthcare, drones, and satellites. Her primary research interests include artificial intelligence, generative AI, machine learning, deep learning, and computer vision. At Feedback Intelligence, Chinar and her team address a crucial challenge in LLM development by automatically converting user feedback into actionable insights, enabling AI teams to analyze root causes, prioritize issues, and accelerate product optimization. This approach is particularly valuable in highly regulated industries, helping enterprises to reduce time-to-market and time-to-resolution while ensuring robust LLM products. Feedback Intelligence, which participated in the Berkeley SkyDeck accelerator program, is currently expanding its business across various verticals.
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// Related Links
Website: https://www.manot.ai/
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