LLM applications are messy, but AdalFlow has made it elegant!
0.2.0 release highlight a unified auto-differentiative framework where you can perform both instruction and few-shot optimization. Along with our own research, “Learn-to-Reason Few-shot In-context Learning” and “Text-Grad 2.0”, AdalFlow optimizer converge faster, more token efficient, and with better accuracy than optimization-focused frameworks like Dspy and text-grad.
Is AdalFlow also focused on automated prompt optimization or is it broader in scope? It looks like there are also some features around evaluation. I'd be really interested to see a comparison between AdalFlow, DSPy [0], LangChain [1] and magentic [2] (package I've created, narrower in scope).
We are broader. We have essential building blocks for RAG, Agents. But also made whatever you build possible to auto-optimize. You can think of us as the library to do in-context learning. Just like PyTorch is for model-training.
We have better accuracy, more token-efficient, and faster convergence speed. We are publishing three research papers to explain this better to researchers.
We will compare with these optimization libraries but wont compare with libraries like LangChain or LlamaIndex. As they simply dont have optimization and it is pain to build on them.
Thanks for the explanation! Do you see auto-optimization as something that is useful for every use case or just some? And what determines when this is useful vs not?
I would say its useful for all production-grad application.
Trainer.diagnose helps you get a final eval score across different splits of datasets: train, val, test, and it logs all errors, including format errors so that you can manually diagnose and to decide if the evaluation is too low that you need further text-grad optimization.
if there is still a big gap between your optimized prompt vs performance on a more advanced model with the same prompt (say gpt4o), then you can use our "Learn-to-reason few-shot" to create demonstration from the advanced model to further close the performance gap. We have use cases optimized the performance all the way from 60% to 94% on gpt3.5 and the gpt4o has 98%.
We will give users some guideline in general.
We are the only library provides "diagnose" and "debug" feature and a clear optimization goal.
0.2.0 release highlight a unified auto-differentiative framework where you can perform both instruction and few-shot optimization. Along with our own research, “Learn-to-Reason Few-shot In-context Learning” and “Text-Grad 2.0”, AdalFlow optimizer converge faster, more token efficient, and with better accuracy than optimization-focused frameworks like Dspy and text-grad.