Optimization of LLM Systems with DSPy and LangChain/LangSmith Share: Download MP3 Similar Tracks Building Effective Agents with LangGraph LangChain Model Context Protocol (MCP), clearly explained (why it matters) Greg Isenberg Omar Khattab, DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines FunctionalTV Building adaptive RAG from scratch with Command-R LangChain Building a self-corrective coding assistant from scratch LangChain Learn LangChain in 7 Easy Steps - Full Interactive Beginner Tutorial Rabbitmetrics Flow Engineering with LangChain/LangGraph and CodiumAI LangChain Reliable, fully local RAG agents with LLaMA3 LangChain DSPy Explained! Connor Shorten Harrison Chase - Agents Masterclass from LangChain Founder (LLM Bootcamp) The Full Stack Chat with Multiple/Large SQL and Vector Databases using LLM agents (Combine RAG and SQL-Agents) Farzad Roozitalab (AI RoundTable) RAG vs. CAG: Solving Knowledge Gaps in AI Models IBM Technology Knowledge Graph or Vector Database… Which is Better? Adam Lucek Bay.Area.AI: DSPy: Prompt Optimization for LM Programs, Michael Ryan FunctionalTV How to Improve LLMs with RAG (Overview + Python Code) Shaw Talebi Reinforcement Learning: Machine Learning Meets Control Theory Steve Brunton How to evaluate agent trajectories with AgentEvals LangChain Achieving Production-level Performance in RAG with DSPy, Parea, and DVC DVCorg LangChain Crash Course for Beginners freeCodeCamp.org Fine-tuning Large Language Models (LLMs) | w/ Example Code Shaw Talebi
