Evidently AI

Hi Makers! I'm Elena, a co-founder of Evidently AI. I'm excited to share that our open-source Evidently library is stepping into the world of LLMs! 🚀 Three years ago, we started with testing and monitoring for what's now called "traditional" ML. Think classification, regression, ranking, and recommendation systems. With over 20 million downloads, we're now bringing our toolset to help evaluate and test LLM-powered products. As you build an LLM-powered app or feature, figuring out if it's "good enough" can be tricky. Evaluating generative AI is different from traditional software and predictive ML. It lacks clear criteria and labeled answers, making quality more subjective and harder to measure. But there is no way around it: to deploy an AI app to production, you need a way to evaluate it. For instance, you might ask: - How does the quality compare if I switch from GPT to Claude? - What will change if I tweak a prompt? Do my previous good answers hold? - Where is it failing? - What real-world quality are users experiencing? It's not just about metrics—it's about the whole quality workflow. You need to define what "good" means for your app, set up offline tests, and monitor live quality. With Evidently, we provide the complete open-source infrastructure to build and manage these evaluation workflows. Here's what you can do: 📚 Pick from a library of metrics or configure custom LLM judges 📊 Get interactive summary reports or export raw evaluation scores 🚦 Run test suites for regression testing 📈 Deploy a self-hosted monitoring dashboard ⚙️ Integrate it with any adjacent tools and frameworks It's open-source under an Apache 2.0 license. We build it together with the community: I would love to learn how you address this problem and any feedback and feature requests. Check it out on GitHub: https://github.com/evidentlyai/e..., get started in the docs: http://docs.evidentlyai.com or join our Discord to chat: https://discord.gg/xZjKRaNp8b.

Hi everyone! I am Emeli, one of the co-founders of Evidently AI. I'm thrilled to share what we've been working on lately with our open-source Python library. I want to highlight a specific new feature of this launch: LLM judge templates. LLM as a judge is a popular evaluation method where you use an external LLM to review and score the outputs of LLMs. However, one thing we learned is that no LLM app is alike. Your quality criteria are unique to your use case. Even something seemingly generic like "sentiment" will mean something different each time. While we do have templates (it's always great to have a place to start), our primary goal is to make it easy to create custom LLM-powered evaluations. Here is how it works: 🏆 Define your grading criteria in plain English. Specify what matters to you, whether it's conciseness, clarity, relevance, or creativity. 💬 Pick a template. Pass your criteria to an Evidently template, and we'll generate a complete evaluation prompt for you, including formatting it as JSON and asking the LLM to explain its scores. ▶️ Run evals. Apply these evaluations to your datasets or recent traces from your app. 📊 Get results. Once you set a metric, you can use it across the Evidently framework. You can generate visual reports, run conditional test suites, and track metrics in time on a dashboard. You can track any metric you like - from hallucinations to how well your chatbot follows the brand guidelines. We plan to expand on this feature, making it easier to add examples to your prompt and adding more templates, such as pairwise comparisons. Let us know what you think! To check it out, visit our GitHub: https://github.com/evidentlyai/e..., docs http://docs.evidentlyai.com or Discord to chat: https://discord.gg/xZjKRaNp8b.

Congratulations on the launch, Evidently team! I've always admired Evidently for its comprehensiveness and all-encompassing approach framework. I often work with teams who are unsure about what metrics to focus on or how to begin their evaluation process. For those new or unsure where to start: * What best practices would you recommend? * Is there a feature that helps beginners 'set things on autopilot' while they're learning the ropes? * Do you offer any guided workflows or templates for common use cases that could help newcomers get started quickly? Thanks for your continued innovations in this space!

Hey Elena and Emeli, How does Evidently AI handle potential biases in the LLM judges themselves? Do you have any plans to incorporate human feedback loops into the evaluation process? Congrats on the launch!

I have to give major respect to the team at Evidently AI for their outstanding open-source product. The introduction of evaluation for LLM apps is a game-changer. It's incredibly easy to integrate into my product, and the monitoring capabilities are top-notch. What I love most is that they provide ready-to-use tests, which easily customizable. Kudos to Evidently AI for making such a valuable tool available to the community!

Incredible launch! Can't wait to try this out! And congrats on the launch @elenasamuylova 🚀

@elenasamuylova and @emeli_dral great that you are building an open-source tool in the LLM evaluation space. Congrats!