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Build Self-Improving AI Agents with DSPy | Relevance AI (No Code)

Ready to build smarter, self-improving AI agents? In this tutorial, we'll show you how to create adaptive AI systems using ... | Channel: Relevance AI

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28

Optimization of LLM Systems with DSPy and LangChain/LangSmith

The most interesting LLM applications are less a single LLM call and more a complex system that relies on multiple LLM calls. | Channel: LangChain

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Getting Started with RAG in DSPy!

Hey everyone! Thank you so much for watching this tutorial on getting started with RAG programming in DSPy! This video will take ... | Channel:...

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Applications of Stanford DSPy for Self-Improving Language Model Pipelines

We delve into practical applications and examples of Stanford DSPy, a programming model designed to enhance the ... | Channel: Databricks

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Intro to DSPy: Goodbye Prompting, Hello Programming!

llm #chatgpt #ai #python #automation #rag #technology #datascience DSPy is a framework developed by Stanford University that ... | Channel: Gao Dalie...

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DSPy prompt optimization - deep tutorial on custom dataset

DSPy prompt optimization DSPy is a framework that nudges you towards machine learning like attitude for LLM prompting. To see ... | Channel: Hacking...

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Streamlining DSPy Development: Track, Debug, and Deploy With MLflow

DSPy is a framework for authoring GenAI applications with automatic prompt optimization, while MLflow provides powerful MLOps ... | Channel:...

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Optimizing LLMs in Insurance with DSPy: Jeronim Morina

In the insurance industry, LLMs promise efficiency but often get bogged down by manual tuning for optimal performance. DSPy ... | Channel: AI Engineer

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🔍 Must-Know AI Trends in August 2024 - Simplifying AI Workflow with DSPy #shorts

DSPy is a new LMP framework created by Stanford University researchers. Full Details: https://github.com/stanfordnlp/dspy ... | Channel: IVIAI Plus

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28

DSPy Part 2 | Technically Speaking

Our teammate Ben brings you DSPy: Part 2 in this episode of 'Technically Speaking'! Learn about ReAct, an agent that allows ... | Channel: Source...

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Topology DSPy: Prompting the Swarm (Multi-Agents)

Latest Tech insights for multi-agent AI by Google. Utilizing DSPy and Topology optimization techniques for an improved ... | Channel: Discover AI

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Bay.Area.AI: DSPy: Prompt Optimization for LM Programs, Michael Ryan

ai.bythebay.io Nov 2025, Oakland, full-stack AI conference DSPy: Prompt Optimization for LM Programs Michael Ryan, Stanford It ... | Channel:...

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How Salomatic is using DSPy & Langtrace for automating medical reports with LLMs. #dspy #ai #llm

Sharing a quick story on how one of our customers is using DSPy and Langtrace for automating the development of patient ... | Channel: Langtrace

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DSPy: The End of Prompt Engineering as We Know It | Boris Cergol | DSC EUROPE 24

Boris discussed the challenges of traditional prompt engineering in LLM application development. He highlighted the ... | Channel: Data Science...

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28

From DSPy to NEW "CoT Encyclopedia" (explain)

Simple task: to improve the intelligence of AI systems beyond given inherent limitations. Improve complex reasoning capabilities ... | Channel:...

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28

Prompt Engineering is Dead; Build LLM Applications with DSPy Framework

Stop prompt engineering in LangChain. You wouldn't hand-select weights of your neural network, so don't hand-select your ... | Channel: Databricks

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Learn to Program Not Write Prompts with DSPy

Writing prompts for our GenAI applications is long, tedious, and unmaintainable. A proper software development lifecycle requires ... | Channel:...

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DSPy: Prompt Tuning Framework

DSPy simplifies prompt tuning for optimal LLM responses. We fine-tune prompts based on input/output analysis, addressing ... | Channel: Convergence...

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28

GEPA Explained!

GEPA is a SUPER exciting advancement for DSPy and a new generation of optimization algorithms re-imagined with LLMs! | Channel: Weaviate vector...

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Automated Prompt Engineering with DSPy

Lifetime access to ADVANCED-inference Repo (incl. DSPy scripts in this vid.): https://trelis.com/ADVANCED-inference/ ... | Channel: Trelis Research

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