Prompt Engineering is Dead; Build LLM Applications with DSPy Framework Share: Download MP3 Similar Tracks Model Context Protocol (MCP), clearly explained (why it matters) Greg Isenberg Stanford Webinar - Agentic AI: A Progression of Language Model Usage Stanford Online Stop Prompt Engineering! Program Your LLMs with DSPy Adam Lucek Unleashing the Potential of Unstructured Data with LLMs and Databricks Databricks Omar Khattab, DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines FunctionalTV RAG vs. CAG: Solving Knowledge Gaps in AI Models IBM Technology AI Engineering at Jane Street - John Crepezzi AI Engineer Evaluating LLM-based Applications Databricks Applications of Stanford DSPy for Self-Improving Language Model Pipelines Databricks The Master Prompt Method: Unlock AI’s Full Potential Tiago Forte Bay.Area.AI: DSPy: Prompt Optimization for LM Programs, Michael Ryan FunctionalTV Knowledge Graph or Vector Database… Which is Better? Adam Lucek AI Engineering in 76 Minutes (Complete Course/Speedrun!) Marina Wyss - Gratitude Driven Building Intelligent AI Agents with Open Source LLMs | smolagents Adam Lucek How might LLMs store facts | DL7 3Blue1Brown Fine Tune DeepSeek R1 | Build a Medical Chatbot DataCamp Get Ready to be Databricks Certified: Generative AI Engineer Associate Databricks How to Build a Multi Agent AI System IBM Technology Transformers (how LLMs work) explained visually | DL5 3Blue1Brown EASIEST Way to Train LLM Train w/ unsloth (2x faster with 70% less GPU memory required) AI Jason
