Quotient AI

I'm excited to launch Evaluations in Quotient's Python SDK ?? Whether you're testing different models, iterating on prompts, or validating outputs against ground truth data, our platform makes evaluating AI applications so easy it's a no-brainer to integrate it in your routine developer workflows. Why did we build this? Talking with engineers from small AI-native startups to large Fortune 500s, we kept hearing the same challenge: the barrier to setting up a reliable evaluation workflow is so high that most prefer to circumvent that process entirely and test manually or in production. This leads to unpredictable performance, undetected regressions, and ultimately, poor user experiences. We believe developers should focus on building amazing products, not babysitting infrastructure. We've been committed from day one to making comprehensive evaluation as simple as possible - just a few lines of code and a few minutes. Behind the scenes, our distributed infrastructure handles all the heavy lifting asynchronously, so you can get back to what matters. Best of all? This is all included in our comprehensive free tier. We provide ?? 10,000 evaluation rows / month, with inference costs included to get you started. ?? Want to see Quotient Evaluations in action? We've published a practical cookbook showing how we used our SDK to evaluate OpenAI's o1 and DeepSeek R1 models on tax-related questions. Find the release announcement and cookbook in comments ?? Curious to see how Quotient's can transform your AI development workflow? Let's chat! Email me directly at julia @ quotientai.co.

Just spotted something that made my day: Miguel from On Project Labs in Spain ???? made a kick-ass video about Quotient AI! Miguel described how Quotient helps him solve real challenges in his AI development process: finding the right prompts, comparing different models, and making smart decisions about which providers to use. He even demonstrated how he used it to generate LinkedIn posts! It's so exciting to see Quotient being discovered and shared organically by developers across the globe. Building tools that actually solve problems for people is what it's all about!

Curious about how AI search engines like Perplexity, Exa, and Google's Gemini measure up? Check out our new Hugging Face cookbook that walks you through systematically evaluating and comparing their outputs using our open source judges ?? library. This comprehensive tutorial provides step-by-step guidance, real-world examples, and practical tips on using LLM-as-a-judge. Link in comments.

Keep your dataset a secret – even from yourself. That’s one of Julia Neagu CEO & Co-founder of Quotient AI, “five rules of evaluations” for bringing AI to production. Explore more tips for deploying large-scale AI – including the best tools and infrastructure, methods for reducing bias, and human-in-the-loop systems – in the latest episode of Data Brew. Hosted by Brooke Wenig and Denny Lee. https://lnkd.in/gV6KijTt

Julia Neagu puts the notion of 'vibe development' to rest in the latest episode of Data Brew. Discover the rules of AI evaluations for bringing products to production in this captivating conversation. Watch the full episode on your favorite podcast platform ?? ?? Apple: https://hubs.ly/Q02Tt0qb0 ?? Spotify: https://hubs.ly/Q02TsYxX0 ?? Youtube: https://lnkd.in/emSpKpuH Data Brew by Databricks is hosted by Brooke Wenig and Denny Lee

Our CEO Julia Neagu chatted with Brooke Wenig and Denny Lee from Databricks about shipping the best AI products ??

We've open-sourced two tools to make LLM evaluation straightforward: - judges ??: A collection of research-backed prompts that use LLMs to evaluate other LLMs. Start evaluating your models immediately with battle-tested approaches. - autojudge: Build your own evaluators that match your team's standards. Feed in your labeled data and feedback, get back evaluator prompts aligned with how your team thinks about quality. There's no need to reinvent the wheel – start with proven LLM-as-a-judge prompts, grow into custom ones tailored to your use-case.

???? we've released `judges` - a OSS library of SOTA, research-backed evaluators for common use-cases like hallucination, harmfulness, and empathy.

??? RAG Systems: Build It, Then Break It (For Science) Building a RAG system is just the beginning. The real challenge? Making sure it performs consistently under pressure. Missed retrievals? Hallucinated answers? Poorly optimized pipelines? These are the bottlenecks that turn promising systems into frustrating ones. In our latest blog, we break down what it takes to go from “it works” to “it works well.” ? Spot retrieval blind spots using precision metrics and relevance testing. ? Fine-tune embedding strategies to ensure accurate context is passed to your LLM. ? Measure and reduce hallucination rates, so your LLM generates grounded, factual responses. We’re talking real frameworks (Ragas, Quotient AI, Arize AI Phoenix), specific fixes for underperforming pipelines, and concrete metrics like NDCG and Recall to measure success. ?? Learn how to evaluate your RAG system: https://lnkd.in/ez2DEEhj

?? ?????????????????? ?????????? ???? ??????-?????????? ?????????????????? ???????????????????? ?????? ?????????????????????? Atita Arora (Solution Architect, Qdrant) and Deanna Emery's (Founding AI Researcher, Quotient AI) talk from the AI Engineer World's Fair is finally available! In the video, they address core challenges in?#RAG-based pipelines and show how to improve desirable metrics like faithfulness, chunk and context relevance step-by-step. You'll get practical tips for evaluating and improving your #RAG-based systems, with real examples that led to an 8% performance boost. ?? Worth checking out https://lnkd.in/dcrrUbxJ