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This edition of DSPyWeekly focuses on the implementation and usage of DSPy with Recursive Language Models. This issue captures all the good articles,...
Announcing the launch of the new Global DSPy Community Map, an interactive directory designed to visualize and connect our worldwide user base. We...
dsprrr brings the power of DSPy to R. Instead of wrestling with prompt strings, declare what you want, compose modules into pipelines, and let...
Today, we are introducing gskill, a fully automated pipeline to learn skills for any repository. Given any GitHub repository, gskill creates...
The RLM paper by Alex L. Zhang, Tim Kraska, Omar Khattab.
Agentic systems often reach a plateau after proof-of-concept because they depend on humans to diagnose edge cases and correct failures. This cookbook...
Welcome back to the Learning DSPy series! So far, we’ve discussed the core building blocks of DSPy (signatures and modules, without going into...
In the rapidly evolving landscape of Large Language Models (LLMs), achieving high-quality, constrained text generation is a common challenge. While...
"Some thoughts related to AI engineering - Prompt Engineering: Writing, versioning and maintaining prompts for pushing model behaviour and swapping...
I (Ankur, curator of DSPyWeekly) have started writing a book on DSPy 🚀 titled - Building AI Applications using Python and DSPy. This is an early...
This notebook demonstrates how to use DSPy’s GEPA (Generalized Error-driven Prompt Augmentation) optimizer to improve language model performance on...
Simple and accessible explanation of RLM and how they work along with code snippets to explain workings and how to use it.
Distillation works like a tutor training a student : a large model teaches a smaller one.1 As we’ve shifted from knowledge retrieval to agentic...
As part of this internship, your task will be to study how the MIPRO optimizer works, implement a similar system in Kotlin, and design its...
Discussion around RLM amongst other things on X on a tweet from Alex posted on 22nd Jan
I’ve kicked off a new DSPy Interview Series to spotlight the people building and pushing the DSPy ecosystem forward. In the first episode, I sat down...
DSPy is a framework for algorithmically optimizing LM prompts and weights, especially when LMs are used one or more times within a pipeline. The...
The article "Prompt Optimization Can Enable AI Control Research" on LessWrong website showcases how automated prompt optimization can significantly...
Interactive Demo built to experience RLM workings.
Omar's ( DSPy creators ) podcast episode with Martin Casado of a16z.