LLMOps Space reposted this
🔥 Two weeks ago we released #TextGrad, our new library for automated prompt optimization. The feedback we got since then has been amazing, with more than 600 stars on GitHub! ⭐ 📕 GitHub: https://lnkd.in/g9grWEwf 📕 Paper: https://lnkd.in/gYg3h7dP A short summary: 1️⃣ TextGrad is an "autograd for text" and provides an automated way to improve prompts with few lines of code. TextGrad's syntax is similar to PyTorch's, so it should all feel very familiar! 2️⃣ TextGrad implements an entire engine for backpropagation through text feedback provided by LLMs, strongly building on the gradient metaphor: We can optimize compound AI systems. 3️⃣ TextGrad can provide feedback on system prompts or coding solutions and optimize them! We have also applied TextGrad to molecule and treatment plan optimization! Amazing work led by Mert Yuksekgonul, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou
Cool stuff! The obvious question is: can you highlight a couple of key differences with respect to DSPy?
Impressive work, can you share benchmarking results?
Hope this can be used for spectrum signal
Very informative
Much congratulations Mert Yuksekgonul & Co. 👏👏
Impressive work on the release of TextGrad, Federico Bianchi! The automated prompt optimization through TextGrad seems quite promising. I am intrigued by its application in optimizing compound AI systems and coding solutions. Looking forward to diving deeper into the GitHub repository and paper for a more detailed understanding.
Awesome
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6moAre there any limitations or challenges in applying gradient-based optimization to text that you've encountered or addressed?