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 accelerate the application development process with text embeddings for sentence and paragraph meaning.

Design and execute real-world applications of vector databases. Build efficient, practical applications, including hybrid and multilingual searches.

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

Learn advanced retrieval techniques to improve the relevancy of retrieved results. Learn to recognize poor query results and use LLMs to improve queries.

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 build and use knowledge graph systems to improve your retrieval augmented generation applications. Use Neo4j's query language Cypher to manage and retrieve data.

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.

Improve your RAG system to retrieve diverse data types. Learn to extract and normalize content from a wide variety of document types, such as PDFs, PowerPoints, and HTML files.

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.

Interact with tabular data and SQL databases using natural language, enabling more efficient and accessible data analysis.

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

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

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

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

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

Understand the transformer architecture that powers LLMs to use them more effectively.

Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback.

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

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

Turn your GenAI prototype into an automated pipeline using Apache Airflow

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