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dspy-0to1-guide/examples/personas/support_sam.py at main · haasonsaas/dspy-0to1-guide · GitHub
Support-Sam: Customer Support with Knowledge Base This persona demonstrates: - RAG-based customer support - Ticket classification and routing - Response generation with knowledge base - Customer satisfaction scoring - Escalation detection
Context Engineering — A Comprehensive Hands-On Tutorial with DSPy
You may have heard about Context Engineering by now. This article will cover the key ideas behind creating LLM applications using Context Engineering principles, visually explain these workflows, and share code snippets that apply these concepts practically.
Free Course - DSPy: Build and Optimize Agentic Apps - DeepLearning.AI
This course teaches you how to use DSPy to build and optimize LLM-powered applications. You’ll write programs using DSPy’s signature-based programming model, debug them with MLflow tracing, and automatically improve their accuracy with DSPy Optimizer. Along the way, you’ll see how DSPy helps you easily switch models, manage complexity, and build agents that are both powerful and easy to maintain.
Learning DSPy: The power of good abstractions • The Data Quarry
In this post, we covered the key abstractions in DSPy, and showed how simple it is to get started. As a developer, you begin by defining signatures, which are a programmatic way to declare your intent and specify the expected input/output types. You then define a custom module (or multiple built-in modules) that call their respective signatures. Signatures and modules depend on adapters under the hood1 to formulate the prompt for the LM to accomplish its task.
🎥 Video
Complete DSPy Course | Automatic and Programmatic Prompt Optimization | Complete Course
How to code an automatic prompt optimizer. How the most advanced prompt optimization tool, DSPy, works and how to fully ... | Channel: Maxime Rivest
Fireside Chat with DSPy Creator w/ Omar Khattab
AI Evals For Engineers & PMs (Cohort starts 6th Oct 2025): https://evals.info Shreya Shankar Interviews DSPy Creator Omar ... | Channel: Hamel Husain
DSPy GEPA Example: Listwise Reranker
Hey everyone! Thanks so much for watching this video exploring DSPy's GEPA optimizer to train a Listwise Reranker! Here is the ... | Channel: Weaviate vector database
Matei Zaharia - Reflective Optimization of Agents with GEPA and DSPy
Channel: Berkeley RDI Center on Decentralization & AI
🚀 Projects
ax-llm/ax
The pretty much "official" DSPy framework for Typescript | Language: TypeScript | License: Apache License 2.0
GitHub - gepa-ai/gepa: Optimize prompts, code, and more with AI-powered Reflective Text Evolution
GEPA (Genetic-Pareto) is a framework for optimizing arbitrary systems composed of text components—like AI prompts, code snippets, or textual specs—against any evaluation metric. It employs LLMs to reflect on system behavior, using feedback from execution and evaluation traces to drive targeted improvements. Through iterative mutation, reflection, and Pareto-aware candidate selection, GEPA evolves robust, high-performing variants with minimal evaluations, co-evolving multiple components in modular systems for domain-specific gains.
danilotpnta/IR2-project
Reproducibility Study of “InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval” This project focuses on a reproducibility study of the InPars Toolkit, a tool designed for generating synthetic data to improve neural information retrieval (IR) systems. Our objective is to replicate and validate the methodology presented in the paper while improving on the future work proposed by the authors.
evalops/cognitive-dissonance-dspy
A multi-agent LLM system for detecting and resolving cognitive dissonance. | Language: Python
AdoHaha/dspy_fun
An introduction to DSPy | Language: Jupyter Notebook | License: MIT License
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