Build production-ready generative AI systems using LLMs, RAG, agents, and full-stack engineering practices
Sub Category
- Data Science
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Objectives
- Design and build production-ready generative AI systems using Large Language Models (LLMs), transformers, embeddings, and modern AI architectures.
- Implement Retrieval-Augmented Generation (RAG) pipelines to ground LLMs in external knowledge, reduce hallucinations, and enable enterprise-grade AI application
- Develop autonomous agentic AI systems using tool calling, multi-step reasoning, memory, and human-in-the-loop controls.
- Create full-stack LLM applications by integrating FastAPI backends, streaming chat interfaces, frontend UX patterns, and stateful memory management.
- Optimize AI systems for cost, latency, and scalability using token optimization, caching strategies, model selection tradeoffs, and load management techniques.
- Evaluate and monitor LLM outputs using human and automated evaluation methods to ensure accuracy, relevance, and faithfulness.
- Apply security, safety, and governance best practices by implementing guardrails, output filtering, policy-based controls, and responsible AI framework
Pre Requisites
- Basic programming knowledge (Python preferred, but not mandatory at an expert level)
- General understanding of APIs or web applications (helpful, not required)
- Curiosity about AI and willingness to build hands-on projects
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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Coupon Code(s)