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Dive into the fascinating world of AI with the #DataStories #BloggingContest by Cleanlab ! 🤖 Check out the coming week's spotlight theme '#LargeLanguageModels'. Share your insights, submit your blogs, and stand a chance to win exciting prizes! 🏆 📝datastories.konfhub.com

An open-source 10x library for anyone working with data: github.com/cleanlab/clean… The team at Cleanlab just finished a presentation for the ml.school community. Their tool is mind-blowing. With a single line of Python code, it will: • Detect common data

Seven Must-Follow Accounts for anyone in the Python, AI/ML space: - Cutting-edge AI research: Sebastian Raschka - Top ML content: Santiago - Python: Mike Driscoll - SOTA MLOps/LLMOps: @AbacusAI - Learn & Build with AI: Lightning AI ⚡️ - Data-centric AI at it's finest: Cleanlab - AI

Cleanlab made the Forbes AI 50 list! It is an honor to be recognized alongside friends and inspirational companies like OpenAI, Databricks, and Hugging Face. We're just getting started... forbes.com/lists/ai50

Come check out the brand new Trustworthy Language Model --- adding trust and reliability to every LLM! The playground is so cool...

Product Announcement: Introducing Cleanlab Studio Auto-Labeling Agent! Annotating a dataset? Save time with Auto-Labeling Agent, which suggests new labels with confidence levels - completing your dataset effortlessly. cleanlab.ai/blog/auto-labe…

Struggling to deploy RAG into production? Our newest video shows how to: - Ensure documents are free of: (near) duplicates, PII, low-quality or non-English text,... - Add smart metadata to improve Retrieval - Gauge the trustworthiness of each LLM answer youtube.com/watch?v=xpDidd…

How many "r" in strawberry?? Today we're excited to announce a new way to catch and explain hallucinations from any LLM! It’s been over a year since the release of GPT-4, but these models remain fundamentally unreliable and risky to use in high-stakes applications. The

What's more exciting than #RAG? Agentic RAG! Checkout my new blog on using LLM trustworthiness scores to automatically optimize retrieval strategy complexity. pub.towardsai.net/reliable-agent…

Introducing Agentic RAG with LLM trustworthiness estimates -- A framework to ensure reliable answers in Retrieval-Augmented Generation and keep latency/costs in check. The idea: Assess response trustworthiness and then adjust retrieval plans to ensure sufficient context [...🧵]

Don’t want users to lose trust in your RAG system? Then add automated hallucination detection. A new benchmark across 4 RAG datasets reveals which detector best flags incorrect AI responses (amongst RAGAS, G-eval, DeepEval, TLM, LLM self-evaluation) 👇 cleanlab.ai/blog/rag-tlm-h…

Want to reduce the error-rate of responses from OpenAI’s o1 LLM by over 20% and also catch incorrect responses in real-time? Just published: 3 benchmarks demonstrating this can be achieved with the Trustworthy Language Model (TLM) framework [...]

NEWS: Cleanlab + Pinecone set a new standard for trustworthy GenAI/RAG! Our latest: AI that’s accurate, curated, and hallucination-free using Cleanlab’ knowledge curation and Pinecone’s vector search. Reliable responses and trust scoring. Full blog 👇pinecone.io/learn/building…

🧹 Hallucination detection from Cleanlab 🧪 The new tlm-langchain package augments your LangChain / LangGraph applications with an LLM trustworthiness score. Cleanlab 's Trustworthy Language Model detects incorrect LLM outputs in real-time via state-of-the-art uncertainty

Let's build a trustworthy RAG app that provides a confidence score for each response:

Better LLMs start with better data and observability We’ve integrated Cleanlab's Trustworthy Language Model (TLM) with Phoenix to help teams improve LLM reliability and performance 🔍 TLM automatically identifies mislabeled, low-quality, or ambiguous training data—ensuring

🚀 New on the MLflow blog: Automatically find the bad LLM responses in your LLM Evals with Cleanlab! Cleanlab’s Trustworthy Language Models (TLM) analyze prompts and responses to calculate a 𝚝𝚛𝚞𝚜𝚝𝚠𝚘𝚛𝚝𝚑𝚒𝚗𝚎𝚜𝚜_𝚜𝚌𝚘𝚛𝚎 — flagging potentially incorrect or

Tomorrow I'm spilling the secrets as to how several Fortune 500 @cleanlabai customers are solving the hardest problem in AI -- producing accurate, compliant, safe fully automated AI Agent responses -- at the AI User Group Conference in SF. Stop by and get your hands dirty and

Cleanlab now works with MLflow — making it easier to detect bad LLM responses right in your pipeline. Faster review cycles. Less manual work. We’re joining MLflow’s upcoming meetup to show how it works. 📅 Attend: lu.ma/mlflow423 📝 Blog: mlflow.org/blog/tlm-traci…

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…