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

Build agents that collaborate to solve complex business tasks.

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

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

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.

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

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

Learn to apply function-calling to expand LLM and agent application capabilities.

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.

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

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.

Efficiently handle time-varying workloads with serverless agentic workflows and responsible agents built on Amazon Bedrock.

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