Use the powerful and extensible LangChain framework, using prompts, parsing, memory, chains, question answering, and agents.

Learn Microsoft's open source orchestrator, Semantic Kernel and use LLM building blocks such as memory, connectors, chains and planners in your apps.

Learn about the latest advancements in LLM APIs and use LangChain Expression Language (LCEL) to compose and customize chains and agents.

Learn advanced RAG retrieval methods like sentence-window and auto-merging that outperform baselines, and evaluate and iterate on your pipeline's performance.

Expand your toolkit with LangChain.js, a JavaScript framework for building with LLMs. Understand the fundamentals of using LangChain to orchestrate and chain modules.

Build a full-stack web application that uses RAG capabilities to chat with your data. Learn to build a RAG application in JavaScript, using an intelligent agent to answer queries.

Build autonomous agents that intelligently navigate and analyze your data. Learn to develop agentic RAG systems using LlamaIndex, enabling powerful document Q&A and summarization. Gain valuable skills in guiding agent reasoning and debugging.

Automate business workflows with multi-AI agent systems. Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language.

Use the AutoGen framework to build multi-agent systems with diverse roles and capabilities for implementing complex AI applications.

Build agentic AI workflows using LangChain's LangGraph and Tavily's agentic search.

Build and fine-tune LLMs across distributed data using a federated learning framework for better privacy.

Learn how to securely fine-tune large language models (LLMs) with private data using federated methods, enhancing data privacy, minimizing risks of data leakage, and optimizing efficiency through Parameter-Efficient Fine-Tuning (PEFT) and Differential Privacy.

Systematically improve the accuracy of LLM applications with evaluation, prompting, and memory tuning.

Learn a flexible framework to build a variety of complex AI applications.

Build agents that navigate and interact with websites, and learn how to make them more reliable.

Build AI apps that access tools, data, and prompts using the Model Context Protocol.

Build, debug, and optimize AI agents using DSPy and MLflow.

Build agents that communicate and collaborate across different frameworks using ACP.