Iryna Kondrashchenko & Oleh Kostromin - Is Prompt Engineering Dead? | PyData Amsterdam 2025
This talk explores various automatic prompt optimization approaches, ranging from simple ones like bootstrapped few-shot to more complex techniques...
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Analyzing content and applying filters...
This talk explores various automatic prompt optimization approaches, ranging from simple ones like bootstrapped few-shot to more complex techniques...
Talk is in Japanese by Tomu Hirata from Databricks In this study session, we will introduce the prompt optimization framework DSPy. This is a...
A comprehensive, production-ready framework for Retrieval-Enhanced Fragmented Reasoning and Generation (REFRAG) that revolutionizes how large...
Build and test signatures interactively. Create multiple cells to experiment with different prompts and see results side by side.
In this blog post, I share my hands-on comparison of DSPy and LlamaBot for building structured LLM applications, using a real-world expense...
Butter is a cache that identifies patterns in LLM responses and saves you money by serving responses directly. It's also deterministic, allowing...
I find it even more painful than usual when something in the DSPyverse is overly complex or insufficiently simplified.
Checked the transcript and there is a short conversation around DSPy. Worth checking for our Japanese readers.
Joe Maddalone live coding extracting description from book images. Coded in typescript port of DSPy.
A collection of GitHub repos for AI engineers.
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This paper, "Unpacking Generative AI in Education," employs computational modeling to analyze the divergent perspectives of teachers and students...
Talks - Compound Retrieval Systems with Connor Shorten, Nova Customization with Vikram Shenoy, Arbor with Noah Ziems, DSPy 3.0 with Omar...
This is a mock summary for the article at https://x.com/highwayvaquero/status/1971314087574618192.
The standard DSPy OpenRouter integration has a critical limitation: it doesn't support model failover and always shows "LiteLLM" as the app name in...
Support-Sam: Customer Support with Knowledge Base This persona demonstrates: - RAG-based customer support - Ticket classification and routing -...
You may have heard about Context Engineering by now. This article will cover the key ideas behind creating LLM applications using Context Engineering...
Multi-Faceted AI Agent and Workflow Autotuning. Automatically optimizes LangChain, LangGraph, DSPy programs for better quality, lower execution...
In-Context Learning for eXtreme Multi-Label Classification (XMC) using only a handful of examples. | Language: Python | License: MIT License
A good code snippet driven style to teach DSPy.