Build production-ready LLM apps using LangChain, RAG, agents, multimodal AI, deployment, and real-world systems
Sub Category
- Data Science
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Objectives
- Understand how large language models work, including tokens, context windows, and inference
- Design effective prompts and prompt strategies for reliable and controllable LLM behavior
- Build modular LLM pipelines using LangChain core components
- Implement Retrieval-Augmented Generation (RAG) systems with embeddings and vector databases
- Design agentic and stateful workflows using LangGraph
- Debug, trace, and evaluate LLM applications using LangSmith
- Build multimodal LLM applications combining text, images, audio, and tools
- Engineer production-ready LLM systems with scalability, reliability, and cost control
- Apply security, safety, and governance best practices to LLM applications
- Test, benchmark, and optimize LLM pipelines for quality, latency, and cost
- Design and deliver a complete end-to-end LLM system as a capstone project
Pre Requisites
- Enthusiasm and determination to make your mark on the world!
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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