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

Learn to break down complex tasks, automate workflows, chain LLM calls, and get better outputs from LLMs. Evaluate LLM inputs and outputs for safety and relevance.

Learn MLOps tools for managing, versioning, debugging, and experimenting in your ML workflow.

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

Learn best practices for prompting and selecting among Meta Llama 2 & 3 models. Interact with Meta Llama 2 Chat, Code Llama, and Llama Guard models.

Learn how to easily build AI applications using open-source models and Hugging Face tools. Find and filter open-source models on Hugging Face Hub.

Learn how to make safer LLM apps through red teaming. Learn to identify and evaluate vulnerabilities in large language model (LLM) applications.

Explore Mistral's open-source and commercial models, and leverage Mistral's JSON mode to generate structured LLM responses. Use Mistral's API to call user-defined functions for enhanced LLM capabilities.

Learn prompt engineering for vision models using Stable Diffusion, and advanced techniques like object detection and in-painting.

Learn Python programming with AI assistance. Gain skills writing, testing, and debugging code efficiently, and create real-world AI applications.

Try out the features of the new Llama 3.2 models to build AI applications with multimodality.

Learn to build with LLMs by creating a fun interactive game from scratch.

Build LLM apps that can process very long documents using the Jamba model

Learn how an AI Assistant is built to use and accomplish tasks on computers.

Build multimodal and long-context GenAI applications using Llama 4 open models, API, and Llama tools.