2025 Bootcamp: Generative AI, LLM Apps, AI Agents, Cursor AI

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Overview

AI Bootcamp: From Zero to Professional in Generative AI and LLM Applications What you'll learn: Key principles of AI and Generative AI Build LLM applications using LangChain, LangGraph, and more Create multimodal and multi-agent LLM apps Deep dive into Retrieval Augmented Generation (RAG) ...

Syllabus

Keys to Artificial Intelligence and the new Generative AI.
LLM Apps with LangChain, CrewAI, LangGraph, LangServe and LangSmith.
How to create an LLM Application from scratch to professional level.
How to build the new Multimodal and Multi-Agent LLM Applications.
Opportunities and threats of AI for businesses, startups, and jobs.
RAG Applications in Depth: Full Stack RAG Apps and Advanced Techniques.
How to manage LLMOps: Observability, Evaluation, Testing, Etc.
Professional opportunities opened by Artificial Intelligence.
Steps to become an Artificial Intelligence Engineer.
How to introduce Artificial Intelligence into your business.
Keys to LLM Applications, the highest potential applications of Generative AI.
Architecture of professional LLM Applications.
The RAG Technique (Retrieval Augmented Generation).
Artificial Intelligence Agents.
Basic and advanced LangChain, LangChain LCEL, and LangChain v010. LangSmith, LangServe, LangChain Templates.
LCEL (LangChain Expression Language) in depth.
Basic and advanced LlamaIndex. LlamaIndex Templates.
ChatGPT, OpenAI, OpenAI functions, and the OpenAI API.
Large Language Models (LLM): ChatGPT, Llama2, Mistral, Falcon, etc.
Vector databases: Postgres, Pinecone, Chroma, FAISS, DeepLake, etc.
Full-Stack Applications: Nextjs and FastAPI.
Professional deployment: Vercel and Render.
Provisional deployment: Streamlit.
Cloud hosting: AWS S3.
How to apply the principles of Responsible AI.
Daily tools of the AI Engineer: Jupyter Notebooks, Python, Terminal, Github, Codespaces, etc.