Cleanlab

Don’t want users to lose trust in your RAG system? Then add automated hallucination detection. Just Published: A comprehensive benchmark of hallucination detectors across 4 public RAG datasets, including: RAGAS, G-eval, DeepEval, TLM, and LLM self-evaluation. See how well these methods actually work in practice for automatically flagging incorrect RAG responses: https://lnkd.in/gq6HiAds

Today, we’re thrilled to share the latest on our Cleanlab + Pinecone partnership, introducing a new standard for building reliable and scalable Retrieval-Augmented Generation (RAG) systems. Let’s use “The Matrix” to explain: What is RAG? 👉 Think “The Matrix” where Neo uploads an entire course into his mind in seconds. Pinecone’s Role: Pinecone is the memory, storing all knowledge Neo (or an AI) needs to take informed actions. Cleanlab’s Role: Cleanlab curates, tags, and stores organized, efficient knowledge so Neo (or an AI) can act accurately and quickly. Sci-fi is becoming reality, and this partnership is a glimpse into that future. Highlights: • Hallucination-Free AI: Cleanlab’s TLM grounds responses in factual sources. • Real-Time Support: Curated knowledge powers quick, accurate responses. • Trust Scoring: Real-time accuracy checks boost reliability. See how these innovations reshape industries in our latest blog post. Big thanks to Pinecone—excited for what’s next! #vectordb #genai #rag #agents #llms #trustworthyai

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: https://lnkd.in/gNY8XfAp TLM wraps any base LLM to automatically: score the trustworthiness of its responses and produce more accurate responses. As of today: o1-preview is supported as a new base model within TLM. The linked benchmarks reveal that TLM outperforms o1-preview consistently across 3 datasets. TLM helps you build more trustworthy AI applications than existing LLMs, even the latest Frontier models.

Worried your AI agents may hallucinate incorrect answers? Now you can use Guardrails with trustworthiness scoring to mitigate this risk. Our newest video shows you how, showcasing a Customer Support application that requires strict policy adherence. If your LLM outputs untrustworthy answers: these automatically trigger a guardrail, which allows you to return a fallback response instead of the raw LLM output, or escalate to a human agent. Adopt this simple framework to make your AI applications significantly more reliable.

The LlamaIndex package offers a rich ecosystem for connecting many LLM models to your own Data, but today's LLMs remain brittle and prone to hallucination. Today we're excited to announce the newest integration available in LlamaIndex: our Trustworthy Language Model, which reliably scores the trustworthiness of every LLM/RAG response to mitigate unchecked hallucinations. Connecting LLMs to Data (ie. RAG) is the first step toward mitigating unchecked hallucination. TLM offers a second step to ensure users don't lose trust in your RAG system. Start using TLM in LlamaIndex here: https://lnkd.in/gkW4_x4E

The Trustworthy Language Model is now natively available in LlamaIndex!

Despite advances from LLMs → RAG → Agentic RAG, today’s AI systems still hallucinate. How can you ensure reliable answers in Retrieval-Augmented Generation, while keeping latency/costs in check? Our newest article demonstrates a system to assess response trustworthiness and adapt processing plans to each query’s complexity. When the currently-generated response is flagged as untrustworthy, our RAG Agent dynamically adjusts Retrieval strategies until sufficient context has been retrieved to generate a trustworthy answer. You can apply this technique to any RAG system and Retrieval strategies. Read the details in today’s publication: Reliable Agentic RAG with LLM Trustworthiness Estimates https://lnkd.in/gCVCn4_H

Adding "Do not Hallucinate" to system prompts is insufficient to achieve reliable AI. Instead use software like Cleanlab that adds trust to model inputs (data) and outputs (responses). Automatically catching issues in data and in LLM responses is the fastest path to reliable AI

What's more exciting than #RAG? AGENTIC RAG! My newest blog is a thought piece that dives into the world of agentic RAG -- how can we utilize #LLM trustworthiness scores to automatically optimize retrieval strategy complexity? The trustworthiness score is my favorite feature of Cleanlab's Trustworthy Language Model.

👀 This study presents 4 new RAG benchmarks. Main finding: The Trustworthy Language Model consistently outperforms approaches like Ragas or DeepEval for automated hallucination detection