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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.
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...
In-Context Learning for eXtreme Multi-Label Classification (XMC) using only a handful of examples. | Language: Python | License: MIT License
This culminates in the ColBERT paradigm for neural IR and in the DSPy framework for natural language programming. We demonstrate through these...
The new BAML adapter (which subclasses the existing JSON adapter) avoids using JSON schema for formatting the data model in the prompt. Instead, it...
DSPy module for OpenAI Codex SDK - signature-driven agentic workflows | Language: Python | License: MIT License
This Databricks blog post discusses building cost-effective, enterprise-grade AI agents using automated prompt optimization - GEPA, MIPROv2, SIMBA...
Build using DSPy.
GEPA, or Genetic-Pareto, is a sample-efficient optimizer based on three principles: Genetic evolution, Pareto filtering, and Reflection using natural...
This course teaches you how to use DSPy to build and optimize LLM-powered applications. You’ll write programs using DSPy’s signature-based...
GEPA (Genetic-Pareto) is a framework for optimizing arbitrary systems composed of text components—like AI prompts, code snippets, or textual...