Prompt Engineering is Dead; Build LLM Applications with DSPy Framework Share: Download MP3 Similar Tracks Applications of Stanford DSPy for Self-Improving Language Model Pipelines Databricks Model Context Protocol (MCP), clearly explained (why it matters) Greg Isenberg Evaluating LLM-based Applications Databricks Stop Prompt Engineering! Program Your LLMs with DSPy Adam Lucek Get Ready to be Databricks Certified: Generative AI Engineer Associate Databricks RAG vs. CAG: Solving Knowledge Gaps in AI Models IBM Technology Unleashing the Potential of Unstructured Data with LLMs and Databricks Databricks MCP vs API: Simplifying AI Agent Integration with External Data IBM Technology Omar Khattab, DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines FunctionalTV Benchmarking LLMs for Enterprise AI | Data Brew | Episode 45 Databricks Introduction to Retrieval Augmented Generation (RAG) and Implementing with Databricks Databricks Fine-tuning Large Language Models (LLMs) | w/ Example Code Shaw Talebi AI Engineering in 76 Minutes (Complete Course/Speedrun!) Marina Wyss - Gratitude Driven How to Build & Sell AI Agents: Ultimate Beginner’s Guide Liam Ottley How might LLMs store facts | DL7 3Blue1Brown Andrew Ng Explores The Rise Of AI Agents And Agentic Reasoning | BUILD 2024 Keynote Snowflake Inc. Building Production-Ready RAG Applications: Jerry Liu AI Engineer How to Improve LLMs with RAG (Overview + Python Code) Shaw Talebi Stanford Webinar - Agentic AI: A Progression of Language Model Usage Stanford Online Optimization of LLM Systems with DSPy and LangChain/LangSmith LangChain
