🍃DSPy vs AdalFlow

About

DSPy stands for Declarative Self-improving Python. It allows you to iterate fast on building modular AI systems and offers algorithms for optimizing their prompts and weights.

AdalFlow borrows PyTorch's design principles by organizing LLM workflows the way PyTorch organizes neural networks: simple building blocks you can combine, debug, and scale.

The key difference is that DSPy focuses on systematic optimization of prompts for accuracy and reliability, while AdalFlow aims at prioritizing efficiency and performance in large-scale deployment.

RAG consists of two steps: information retrieval and answer generation. DSPy and AdalFlow cannot enhance the retrieval step since it relies on embeddings, ranking algorithms, or index structure. Therefore, they focus solely on optimizing the answer generation step.

Also note the versions of these 2 libraries used in following experiments: