Developers, say goodbye to tedious LLM prompt-testing. Introducing an intuitive platform that simplifies LLM prompt engineeringqueryvary.com SingaporeJoined June 2023
This new feature is designed to amplify your creativity and control in crafting prompts, allowing you to quickly determine your prompt performance and test different cases
Happy Prompting and Happy New Year! 🎇
#AI#CustomVariables#PromptEngineering#Innovation
Custom Variables are here to enhance your prompt engineering experience. Now you have the power to define your own variables and values, unlocking a world of limitless customization in your AI interactions.
🌟 Exciting News from Query Vary as We Wrap Up the Year! 🌟
As we approach the year's end, our team is still hard at work offering innovative solutions for you! This week, we're thrilled to unveil a game-changing feature: Custom Variables! 🛠️
🎉New Feature Alert🎉
The unfortunate truth is that LLMs are constantly changing. Prompts that are reliable this month might not have the same output a few months from now.
Continuous testing of prompts are therefore necessary.
To address this issue, the Query Vary team have introduced a new feature that will allow you to automate your tests! So no more worrying about the reliability and accuracy of your prompt output ✨
Learn how to use The Scheduler here 👇
queryvary.com/post/automated…
Credits to @kadirtorrico this is a rather accurate depiction of what @queryvary does!
Playground now also supports collaboration with teammates, automated testing , hooking up your vector database and more
We've had quite a few updates this month!
- Invite team members
- Support for CLAUDE 2 and PaLM 2
- Hook up your own vector database
and many more
Read about it in our latest blog post
queryvary.com/post/query-var…
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Where do you think the models failed or did good?
Was it the prompt provided or the quality of the model? Let me know in the replies/quotes.
Create your own tests with Query Vary here: app.queryvary.com/signup/#AI#nlp#MachineLearning
CLAUDE 2 performed the best 🎉.
GPT-3.5 was able to extract the data but not create a table.
Bison was able to create a table but failed to extract the data from the embedding completely.
Remember, as AI continues to evolve, techniques like these will be invaluable in ensuring we get the most out of our systems. Stay curious, stay updated, and always be open to learning more! 🌟 #AI#LanguageModels#PromptEngineering
The crux? Chaining prompts using routers and subchains makes AI more robust, efficient, and adaptable. It's not about the perfect prompt, but the process of developing tailored prompts. 🔄🎯
Ever wonder how to elevate the responsiveness of your AI system? Let's dive into a technique called 'chaining prompts'. Imagine having separate prompts for physics, math, history, & comp science. Each prompt is specialized in its domain. 📚💻
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