SuperAnnotate

Check out the 1st episode of our new SuperAnnotate #AIin10 video series! In this episode Jason Liang and I talk about: ✅ When to use LLM as a judge and when to rely on human review ✅ How to set up a cost-efficient human review process using tiered expertise ✅ How to correctly examine your AI Agent data Link to the episode in comments 👇 DM us or comment if you’d like to learn more about any of these topics! #aievals #aiin10 #ai

Many AI Infra Providers are pushing LLMs-as-judges as the only way to evaluate models and agents. It’s cheap, you get to production quickly, and honestly - these providers just don’t have the tooling to bring human experts into the loop the right way. But now that companies following this approach are in production - the cracks are showing. 𝗙𝘂𝗹𝗹𝘆 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗹𝗮𝗰𝗸𝘀 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁, 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁𝗹𝘆—𝘁𝗵𝗲 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝘁𝗵𝗮𝘁 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗱𝗲𝗺𝗮𝗻𝗱. At SuperAnnotate, we’ve always known that humans need to stay in the loop, and work in tandem with LLM judges. That’s why we’ve been partnering with leading AI enterprises to help them create the optimal combination of LLM-based evals with subject-matter experts, creating systems that are: ✅ High-quality ✅ Cost-efficient ✅ Fast to deploy ✅ Built on trust We just published a whitepaper outlining what’s working. If you’re thinking about evals, it’s worth a read: 👉 https://lnkd.in/etpQXEVk #GenerativeAI #LLMEvaluation #MLOps #AIProduct #EnterpriseAI #SuperAnnotate #HITL #Agent #LLMOps

❌ Off-the-shelf models for agents fail. ✅ Evaluated models win. See how SuperAnnotate helps AI leaders build agents that perform in the real world.

📣 Join SuperAnnotate and Amazon Web Services (AWS) for a deep dive into building reliable LLM Judge systems that scale AI evaluation. Learn how leading AI teams design judges by leveraging models hosted on AWS Bedrock, and then refine their accuracy through human-in-the-loop annotation on SuperAnnotate. Register today 👉 https://lnkd.in/gdVTGj4W

Most AI Agent failures aren’t technical, they’re evaluation failures. POCs stall because teams lack a structured way to measure performance, leaving ML teams without the data they need to improve, and leadership without the confidence to make a launch decision. That’s why we put together The Practical Guide to Evaluating Agentic AI Systems. 🎓 You’ll learn how to: - Define metrics that align with business goals - Combine human-in-the-loop review with LLM judges - Build LLM judges that really work - And the processes for evaluation in development and in production Read the full guide: https://lnkd.in/dCvUHp9g

Agent talk is everywhere. But how do you evaluate their quality? Is this agent going to touch external users/customers? What is the cost to the business of a bad/wrong answer? Learn how to involve your internal stakeholders in the evaluation process and get XFN signoff for launch with our Agent Evaluation Whitepaper (link in comments). SuperAnnotate helps you setup a repeatable framework for shipping Agents putting people at the center of the process. Even better, join us for an Eval workshop where we will build you a custom workflow based off your project requirements and sample data. Fully integrated to your data and models on Fireworks AI , NVIDIA, Amazon Web Services (AWS), Databricks, Google Cloud, OpenAI, Anthropic