🔍 Unlock generative AI success with quality data! Join #AWS & Cleanlab for an exclusive workshop at SFO Gen AI Loft on May 9, 2025. Learn to build & scale production-ready AI solutions from experts. For developers & decision-makers. Register now! 👉 go.aws/3SeOxwU

New: Langtrace.ai now includes native support for Cleanlab! Log trust scores, explanations, and metadata for every LLM response—automatically. Instantly surface risky or low-quality outputs. 📝 Blog: langtrace.ai/blog/langtrace… 💻 Docs: docs.langtrace.ai/supported-inte…

Curious about how to systematically evaluate and improve the trustworthiness of your LLM applications? 🤔 Check out how Cleanlab's Trustworthy Language Models (TLM) integrates with #MLflow! TLM analyzes both prompts and responses to flag potentially untrustworthy outputs-no

New integration: Cleanlab + Weaviate • vector database! Build and scale Agents & RAG with Weaviate. Then add Cleanlab to: - Score trust for every LLM response - Flag hallucinations in real time - Deploy safely with any LLM 📷weaviate.io/developers/int…

Introducing the fastest path to AI Agents that don't produce incorrect responses: - Power them with your data using LlamaIndex 🦙 - Make them trustworthy using Cleanlab

We asked the Databricks AI + Data Summit chatbot where the Cleanlab booth was. It replied: “I couldn’t find any information…” and spit out some code. 🤖💥 It's a good thing we’re here! We exist to make AI answers more trustworthy, even at AI conferences. 😎

Singapore Government just dropped the Responsible AI Playbook - not just talk, but actual technical guidance for deploying AI systems safely. Their key recommendations: - LLMs are like "Swiss Cheese" - full of unpredictable capability holes. - Guardrails for reliable LLM apps

We’re on a quest to make customer support chatbots more trustworthy. 🤖 Our new case study with LangChain shows how to catch hallucinations and bad tool calls in real time using Cleanlab trust scores. LangGraph fallbacks make fixing them easy 👇 cleanlab.ai/blog/prevent-h…

🛑Prevent Hallucinated Responses Our integration with Cleanlab allows developers to catch agent failures in realtime To make this more concrete - they put together a blog and a tutorial showing how to do this for a Customer Support agent Blog: cleanlab.ai/blog/prevent-h…

How to build support agents that are safe, controllable, work, and keep you out of the news. Use Cleanlab directly integrated with LangChain. Cleanlab is the most integrated and most accurate real-time safety/control layer for Agents/RAG/AI.

MIT Startup Exchange (STEX) startup spotlight: Cleanlab MIT startup @Cleanlab partners with NVIDIA to tackle the biggest problem in Enterprise AI: outputs you can trust. Full story: developer.nvidia.com/blog/prevent-l…

🤖 Building with OpenAI’s Agents SDK? This new tutorial shows how to catch low-trust outputs before they reach customers. • Auto-handle incorrect AI responses • Prevent failures in multi-agent handoffs • Improve reliability without retraining 👉 help.cleanlab.ai/tlm/use-cases/…

🤖 🛡️ Cleanlab Trust Scoring Cleanlab's powerful trust scoring system prevents AI hallucinations in customer support, seamlessly integrating with LangGraph to detect and block problematic responses before reaching users. Explore the technical implementation here:

AI agents don’t just fail from hallucinations. They fail when tool calls go wrong—wrong tool, bad input, skipped step. We dropped a new tutorial to score tool calls for trust so you can catch failures early, before they hit users. 👉 help.cleanlab.ai/tlm/tutorials/…

Let's build a "Chat with your Code" RAG app using Qwen3-Coder:

Automate detection of unreliable LLM outputs by combining MLflow tracing with Cleanlab's Trustworthy Language Models (TLM). 🚀 This blog post covers: ✅ Setting up MLflow to capture complete LLM interactions, including system prompts. ✅ Retrieving traces and efficiently

If your AI agent makes a mistake, Cleanlab can either provide a more reliable response or flag the case for human review. New tutorial: Add Cleanlab as a trust layer for any conversational agent. 👉help.cleanlab.ai/codex/tutorial…

💡 Trust Scoring = More Reliable AI Agents AI engineer Gordon Lim's latest study shows that trust scoring reduces incorrect AI responses by up to 56% across popular agents like Act, ReAct, and PlanAct. 🔍 Explore the full study: medium.com/data-science-c…