Learn the fundamentals of prompt engineering for ChatGPT. Learn effective prompting, and how to use LLMs for summarizing, inferring, transforming, and expanding.

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

Learn how to prompt an LLM to help improve, debug, understand, and document your code. Use LLMs to simplify your code and enhance productivity.

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

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

Learn how to create an automated CI pipeline to evaluate your LLM applications on every change, for faster and safer development.

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.

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.

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.

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.

Optimize the efficiency, security, query processing speed, and cost of your RAG applications.

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

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.

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

Build systems with MemGPT agents that can autonomously manage their memory.

Move your LLM-powered applications beyond proof-of-concept and into production with the added control of guardrails.

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

Learn to use OpenAI Canvas to write, code, and create more effectively in collaboration with AI.

Learn how to use and prompt OpenAI's o1 model for complex reasoning tasks.

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.

Learn how to systematically evaluate, improve, and iterate on AI agents using structured assessments.

Learn to build, debug, and deploy applications with an Agentic AI-powered integrated development environment.

Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management.

Design, build, and deploy apps with an AI coding agent in an integrated web development environment.

Learn how to generate structured outputs to power production-ready LLM software applications.

Build agents that write and execute code to perform complex tasks, using Hugging Face’s smolagents.

Build responsive, scalable, and human-like AI voice applications.

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

Improve LLM reasoning with reinforcement fine-tuning and reward functions.

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

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