Huge shout out to our dev team for making this happen over the past few weeks. Through close collaboration with our customers, we successfully shipped these crucial features on our platform. Check the comments section for the video links from the slides.
LangWatch
Softwareontwikkeling
Amsterdam, North Holland 632 volgers
Improve your LLM app with Monitoring & User analytics
Over ons
AI solutions are becoming fundamental to businesses of all sizes. Langwatch provides all-in-one tooling to improve and iterate on your current models, as well as mitigating risks such as data leakage, jailbreaking and hallucinations. To create AI with confidence, create it with Langwatch.
- Website
-
https://langwatch.ai/
Externe link voor LangWatch
- Branche
- Softwareontwikkeling
- Bedrijfsgrootte
- 2-10 medewerkers
- Hoofdkantoor
- Amsterdam, North Holland
- Type
- Particuliere onderneming
- Opgericht
- 2023
Locaties
-
Primair
Fred. Roeskestraat 115
Amsterdam, North Holland 1016, NL
Medewerkers van LangWatch
-
Richard Huth
Founding Engineer | LangWatch.ai
-
Manouk Draisma
📊 Monitor & improve the quality & safety of your (Gen)AI solutions and ship with confidence.
-
Rogério Chaves
Co-Founder @ LangWatch - Measure the quality and continuously improve your LLM apps
-
Yevhenii Budnyk
AI Developer and Entrepreneur
Updates
-
LangWatch heeft dit gerepost
Follow along with this tutorial by Gabriel Luiz Freitas Almeida and Felipe Rosa to learn how to build hierarchical/sequential agent flows using CrewAI, and how to create evals with LangWatch using the latest integrations in Langflow. Youtube Link: https://lnkd.in/d6-49HC5
-
-
LangWatch heeft dit gerepost
We are thrilled to share that LangWatch is the official judge for the Langflow (by DataStax) Hackathon Competition! 🎉 This hackathon, focused on their large Brazilian community, is an incredible opportunity for the 3500 AI developers to showcase their skills in developing the best RAG applications. Our platform's evaluators will be diving into the quality of these RAG applications to pick the ultimate winner. The competition kicks off today, and we can't wait to see the creativity and expertise in action! 🤩 This is a fantastic way for us to showcase the best methods for evaluating LLM quality. Good luck to all participants! 🔥 #LangwatchAI #LangflowHackathon #Innovation #AI #LLM #Hackathon
-
-
We are thrilled to share that LangWatch is the official judge for the Langflow (by DataStax) Hackathon Competition! 🎉 This hackathon, focused on their large Brazilian community, is an incredible opportunity for the 3500 AI developers to showcase their skills in developing the best RAG applications. Our platform's evaluators will be diving into the quality of these RAG applications to pick the ultimate winner. The competition kicks off today, and we can't wait to see the creativity and expertise in action! 🤩 This is a fantastic way for us to showcase the best methods for evaluating LLM quality. Good luck to all participants! 🔥 #LangwatchAI #LangflowHackathon #Innovation #AI #LLM #Hackathon
-
-
🚀 Yesterday, Rogério Chaves took the stage at AI Builders (Join this community if you haven't yet) to speak about a more and more popular topic: Automated prompt engineering! 👏 Kudos to Rogerio for an incredible session 🌟 He showcased optimizing a RAG solution and visualized the results using the DSPy framework and visualizer. Interested in learning more about this topic and missed the session? Reach out to us via: https://lnkd.in/eN_PpebD and mention "auto-prompting" in the comments. #AI #DSPy #AutomatedPromptEngineering #Innovation
-
-
🚀 Exciting News for LLM Enthusiasts! 🚀 Have you started building your LLM-powered solutions with FlowiseAI (YC S23) We’re excited to share that LangWatch is now fully integrated into Flowise! 🌟 Build your perfect AI solution while we handle monitoring, quality checks, and provide full visibility into your user analytics. Say goodbye to the black-box and gain deeper insights into your AI's performance. #AI #LLM #genai #llmops #monitoring #qualitycontrol #UserInsights
Flowise v1.8.3 release 🎊 New models - Claude Sonnet 3.5 - Azure gpt-4o - Voyage-2 embeddings and reranker models Chat Embed - Agent reasoning steps - Tooltip display - Notification sound You can find the all the configurations: https://lnkd.in/ebQ3HrnQ 🔥 FireCrawl Web Scraper Crawl and convert any website into LLM-ready markdown or structured data in Flowise, thanks Mendable team for the integration! 🔭 LangWatch observability New observability tool in Flowise! Docs: https://lnkd.in/eAj77dXB 📚 Multi Query Retriever 1. Generate multiple queries for a given user question 2. For each query, retrieves a set of relevant documents 3. Takes the unique union across all queries for relevant documents This significantly enhances the final result due to the comprehensive answers from multiple perspectives! For more bugfixes and improvement details, check out the release page: https://lnkd.in/eTKvkJXh
-
Excited to share that LangWatch is at The AI Summit in London today and tomorrow! 🇬🇧 Visit us at the Dutch booth, "The Garden," where we’ll be joined by other amazing startups. Don't miss the opportunity to stop by and meet with us! Thanks to Rijksdienst voor Ondernemend Nederland (RVO) for the organisation.
-
-
"After many hours of manual prompt engineering we couldn't get the accuracy higher than 60%, DSPy took it to 80% for us in a matter of minutes." DSPy is the hot new framework on LLM community, which takes a different approach than what so far has been done for the past year: instead of having engineers manually trying to adjust prompt to improve results and do a *vibe-checking* on the outputs. That's why LangWatch implemented the DSPy Visualizer, which allows you to log your DSPy training sessions, track the performance, costs, compare runs and debug them in detail on the LangWatch platform. Read more here: https://lnkd.in/e_e8e_m4 Curious to try it out? Book a demo with us: https://lnkd.in/eN_PpebD #dspy #promptengineering #llms #genai
-
Preventing 'bullshit' output in LLM-powered applications is crucial for ensuring reliable and trustworthy AI interactions. Setting up the right quality evaluations is key to mitigating this issue. Here are some tips to get started: 1. Establish Baselines: Define clear baselines for what constitutes acceptable and unacceptable responses. This helps in identifying deviations more effectively. 2. Context Relevancy: Evaluate whether the AI’s responses are contextually relevant. This means checking if the information provided aligns with the query and context given. 3. Faithfulness: Ensure that the responses are faithful to the provided context and do not introduce information that cannot be inferred from it. 4. Use Custom Evaluations: Leverage custom evaluations tailored to your specific use case. Generic metrics often fall short, so customize your evaluations to focus on your application's unique requirements. Lastly, continuously monitor and review the product performance. By implementing these strategies, you can significantly reduce hallucinations and enhance the reliability of your LLM-powered application. Stop nonsense output before they start – set up the right evaluations today! Sign-up for a Expert Call with one of the specialists on this topic: https://lnkd.in/eN_PpebD or Check-out LangEvals: https://lnkd.in/e9FTb6aD
Request a demo
get.langwatch.ai
-
Our dev team is speedy 🏎️ 💨 as well tonight. Adding GPT4o to the new playground feauture to compare your models and use the best chosen model for your AI use case. Reach out if you want to learn more. https://lnkd.in/eN_PpebD
NEW GPT-4o VS LLAMA 3 SPEED TEST 🏎️💨 OpenAI just announced their new model GPT-4o with impressive capabilities, taking multimodal to a whole new level which definitively took the spotlight, but not only that, GPT-4o is smarter, cheaper and way faster And today we at LangWatch are releasing publicly our playground feature, and making GPT-4o immediately available on it, you can compare with other models, like the race against Llama 3 on Groq on the video below, it's impressive to watch! GPT-4o is also immediately available for all our LLM evaluators, meaning you can automatically make your LLM evaluators more powerful for half of the price per token! https://app.langwatch.ai