UpTrain (YC W23)’s Post

🚀 Exciting Update for LLM Developers! 🚀 Delighted to announce a new integration between UpTrain and Promptfoo, aimed at enhancing prompt experimentation for LLM developers. What does this mean for you? 🔍 Compare with Ease: Easily compare outputs from different LLM models and prompt versions. 📊 Analyze Performance: Dive into UpTrain's metrics to evaluate performance across experiments. 📈 Visualize Insights: Utilize Promptfoo's dashboards to visualize experiment results. Whether you're fine-tuning a model or exploring new avenues, this integration equips you with the tools to innovate effectively. Ready to elevate your experimentation? Explore the integration today! #AI #MachineLearning #LanguageModels #UpTrain #Promptfoo #Experimentation

🚀 As a contribution to the open-source community, we have open-sourced our dashboards on GitHub. 🔧 What does that mean? You can now run UpTrain dashboards locally on your devices in just 3 simple steps! To get started: 1. Clone the UpTrain repository 2. Run the bash command 3. Launch the dashboards Check out the GitHub repo. Link in comments #OpenSource #LLMEvaluation #Dashboards

🚀 Latest update in UpTrain! UpTrain can now simulate and evaluate conversations with AI assistants. Simulate Conversations: Easily simulate conversations with AI assistants for different scenarios. Evaluate Conversations: Evaluate the performance of the assistant based on metrics like user satisfaction, factual accuracy, relevance, and many more. Try it out using: https://lnkd.in/g7UqXKY2

Implementing RAG to an LLM application seems easy, but building a fully functional RAG pipeline is a lot more challenging. A lot of factors can go wrong: - The retrieved context is poor. - The context is not getting utilized effectively. - The LLM is hallucinating, generating incorrect information. and a lot more… These challenges can lead to incomplete or inaccurate responses, undermining the reliability of the LLM system. To understand more about the different problems that can occur in RAG and how to solve them, check out our recent blog: https://lnkd.in/gRCZUMy8

🚀 Introducing our new dashboards, designed to enhance your LLM applications evaluation experience: 1️⃣ Evaluate LLM Applications: Use metrics like relevance, factual accuracy, and more to measure the performance of your LLM applications. 2️⃣ Compare Prompts: Easily compare different versions of prompts to choose the best fit for your use case. 3️⃣ Build Your Own Experiments: Create and manage experiments effortlessly. 4️⃣ Set Up Daily Monitoring: Keep track of your progress with daily monitoring graphs, ensuring your LLM applications are always performing at their best. Check out these dashboards here: https://lnkd.in/gaSYt8Ev #UpTrain #LLM #AI #MachineLearning #Dashboards #Productivity

Tired of managing changes in your LLM experiments? Something big is coming up! 🚀

🚀 We're excited to introduce the latest enhancements to UpTrain: New Integrations: Ollama: Run evaluations using LLM models hosted locally on your system. Langfuse (YC W23): Easily track your LLM applications for latency, cost, and more. Promptfoo: Conduct experiments to compare prompts and models, visualize results on Promptfoo's dashboards. Zeno: Dive deep into your LLM experiments with interactive dashboards. Helicone: Monitor your LLM applications with detailed dashboards. Automatic Failure Case Identification: UpTrain now automatically identifies failure cases, including issues related to poor quality of retrieved context or inadequate utilization of context, among other challenges. Custom Evaluations: Add Python code and define your own evaluations, such as identifying repetition of words in generated content or analyzing other complex patterns! Upgrade to the latest release of UpTrain (v0.6.10.post1) to check out these updates! 🌟