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ajosh0504 merged 1 commit into from
Sep 22, 2025
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Adding links to hands-on workshops #156

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We have designed a series of hands-on workshops to take you from zero to hero with Generative AI. Here's a list of workshops we currently have:

|Workshop name | Link | Description | Language | Tools used |
|Workshop name | Links | Description | Language | Tools used |
|------------|-------------|----------------|-------------|-------------|
| Vector Search: Beginner to Pro | [Start Lab](https://mongodb-developer.github.io/vector-search-lab/) | Learn vector search concepts such as embeddings, how vector search works in MongoDB, and also advanced concepts such as pre-filtering, vector quantization etc. Gain hands-on experience by building a multimodal vector search application for an online library. | Python | MongoDB Atlas, HuggingFace, OpenAI (CLIP)|
| Building RAG Applications with MongoDB | [Start 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, HuggingFace, Anthropic/Azure OpenAI/Gemini |
| The A to Z of Building AI Agents | [Start 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, HuggingFace, Anthropic/Azure OpenAI/Gemini, LangGraph |
| Pragmatic LLM Application Development: From RAG Pipelines to AI Agents | [Start 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 |
| Vector Search: Beginner to Pro | [Slides](https://docs.google.com/presentation/d/e/2PACX-1vR4lPTcr2ZXPTkQLPq3HtTn4vSLG4VrFD3jkOjXmEDrrvyLEElTaz-6JC5KZN4__VJZ2h13aTabGXhG/pub), [Hands-on Lab](https://mongodb-developer.github.io/vector-search-lab/) | Learn vector search concepts such as embeddings, how vector search works in MongoDB, and also advanced concepts such as pre-filtering, vector quantization etc. Gain hands-on experience by building a multimodal vector search application for an online library. | Python | MongoDB Atlas, Voyage AI|
| 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 |

## Questions?

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