| Building RAG Applications with MongoDB | [Slides](https://docs.google.com/presentation/d/e/2PACX-1vSN_7zZTqpXSmtUyDalox2kAoealuO4V_aVGqLuTuDKa3I3aJ9nQUdViQKasBNnu2zQVOpT5cubnyFd/pub), [Hands-on Lab](https://mongodb-developer.github.io/ai-rag-lab/) | Learn RAG concepts such as embedding, chunking and vector search. Gain hands-on experience by building a RAG-based chatbot for a technical documentation website. | Python | MongoDB Atlas, Voyage AI, Anthropic/Azure OpenAI/Gemini |

| The A to Z of Building AI Agents | [Slides](https://docs.google.com/presentation/d/e/2PACX-1vRMH-7DLejrxrEgrReZTy4p9sKzN35uTaiDRZ8JAM9xtyLFz-utjJzk97FG8mGI96VEuLPnLZWzq10Q/pub), [Hands-on Lab](https://mongodb-developer.github.io/ai-agents-lab/) | Learn the core concepts of AI agents, such as reasoning, tools, and memory. Gain hands-on experience by buiding a technical documentation AI agent. | Python | MongoDB Atlas, Voyage AI, Anthropic/Azure OpenAI/Gemini, LangGraph |

| Pragmatic LLM Application Development: From RAG Pipelines to AI Agents | [Hands-on Lab](https://github.com/mongodb-developer/GenAI-Showcase/blob/main/notebooks/workshops/Pragmatic_LLM_Application_Introduction_From_RAG_to_Agents_with_MongoDB.ipynb) | Build RAG pipelines and AI agents with and without abstraction frameworks such as LangChain. Also introduces techniques such as prompt compression for optimizing LLM apps. | Python | MongoDB Atlas, OpenAI, LangChain, LLMLingua |