📜 Citation & Reading More

📜 Citation & Reading More

If you're looking to understand the framework, please go to the [DSPy Docs at dspy.ai](https://dspy.ai).

If you're looking to understand the underlying research, this is a set of our papers:

**[Jul'25] [GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning](https://arxiv.org/abs/2507.19457)**       
**[Jun'24] [Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://arxiv.org/abs/2406.11695)**       
**[Oct'23] [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://arxiv.org/abs/2310.03714)**     
[Jul'24] [Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together](https://arxiv.org/abs/2407.10930)     
[Jun'24] [Prompts as Auto-Optimized Training Hyperparameters](https://arxiv.org/abs/2406.11706)    
[Feb'24] [Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models](https://arxiv.org/abs/2402.14207)         
[Jan'24] [In-Context Learning for Extreme Multi-Label Classification](https://arxiv.org/abs/2401.12178)       
[Dec'23] [DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines](https://arxiv.org/abs/2312.13382)   
[Dec'22] [Demonstrate-Search-Predict: Composing Retrieval & Language Models for Knowledge-Intensive NLP](https://arxiv.org/abs/2212.14024.pdf)

To stay up to date or learn more, follow [@DSPyOSS](https://twitter.com/DSPyOSS) on Twitter or the DSPy page on LinkedIn.

The **DSPy** logo is designed by **Chuyi Zhang**.