Microsoft
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Microsoft
AWS
DataBricks
Adobe
Baidu
IBM
Accenture
Cisco
Intuitive and explainable metrics to quantify performance of your LLM app
Reduce developer time to evaluate by 90% generating synthetic test datasets
Custom smaller models for monitor the quality of LLM application in production
LlamaIndex + RAGAs Cookbook 🧑🍳 The first step to building any advanced RAG application is defining quality metrics, and RAGAs (@Shahules786) is a popular framework to comprehensively evaluate a RAG app component-wise and e2e. Metrics: Context relevance/recall/precision,…
Webinar on evaluating RAG systems going live in 5 minutes! Excited to be joined by the RAGAS team crowdcast.io/c/bnx91nz59cqq
💪RAGAS is an awesome open-source framework for evaluating RAG systems 🤝We recently integrated LangSmith <> RAGAS for optimal ease of evaluation 🎥We're also doing a webinar on RAG evaluation with them in 5 minutes!!! Link to webinar and blog 👇
Congrats to @shahules786 for creating the only RAG framework directly recommended by openai at devday Thought leadership is the art of nailing the highest order bit
Great overview of RAGAS! We also did a webinar with them 2 weeks ago - check it out for more insights of how to evaluate RAG systems! youtube.com/watch?v=fWC4Vx…
RAGAS 🤝 Weaviate! New podcast tomorrow, some awesome background info below! 👇
RAG evaluation is the next step once your pipeline is set ☝️ There are four new metrics based on LLM evaluation that are setting the standard: 1. Faithfulness, 2. Answer relevancy, 3. Context precision, and 4. Context recall (Ragas score) The visual below illustrates prompting…
💪RAGAS is an awesome open-source framework for evaluating RAG systems 🤝We recently integrated LangSmith <> RAGAS for optimal ease of evaluation 🎥We're also doing a webinar on RAG evaluation with them in 5 minutes!!! Link to webinar and blog 👇
I am blown away by RAGAS With 10 lines of code, I created a question + answer dataset of Airbnb's latest annual report (10-K). The dataset has 3 parts: • questions • contexts • ground truth answers Next step: Evaluate how well various LLMs perform RAG on financial…
LlamaIndex + RAGAs Cookbook 🧑🍳 The first step to building any advanced RAG application is defining quality metrics, and RAGAs (@Shahules786) is a popular framework to comprehensively evaluate a RAG app component-wise and e2e. Metrics: Context relevance/recall/precision,…
Webinar on evaluating RAG systems going live in 5 minutes! Excited to be joined by the RAGAS team crowdcast.io/c/bnx91nz59cqq
💪RAGAS is an awesome open-source framework for evaluating RAG systems 🤝We recently integrated LangSmith <> RAGAS for optimal ease of evaluation 🎥We're also doing a webinar on RAG evaluation with them in 5 minutes!!! Link to webinar and blog 👇
Congrats to @shahules786 for creating the only RAG framework directly recommended by openai at devday Thought leadership is the art of nailing the highest order bit
Great overview of RAGAS! We also did a webinar with them 2 weeks ago - check it out for more insights of how to evaluate RAG systems! youtube.com/watch?v=fWC4Vx…
RAGAS 🤝 Weaviate! New podcast tomorrow, some awesome background info below! 👇
RAG evaluation is the next step once your pipeline is set ☝️ There are four new metrics based on LLM evaluation that are setting the standard: 1. Faithfulness, 2. Answer relevancy, 3. Context precision, and 4. Context recall (Ragas score) The visual below illustrates prompting…
💪RAGAS is an awesome open-source framework for evaluating RAG systems 🤝We recently integrated LangSmith <> RAGAS for optimal ease of evaluation 🎥We're also doing a webinar on RAG evaluation with them in 5 minutes!!! Link to webinar and blog 👇
I am blown away by RAGAS With 10 lines of code, I created a question + answer dataset of Airbnb's latest annual report (10-K). The dataset has 3 parts: • questions • contexts • ground truth answers Next step: Evaluate how well various LLMs perform RAG on financial…
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