LLM Engineering: Prompting, Fine-Tuning, Optimization & RAG
About this course
This specialization teaches end-to-end LLM engineering—from prompt design and evaluation to fine-tuning workflows, model optimization, and retrieval-augmented generation (RAG). You’ll learn to build robust LLM applications with measurable quality, safer outputs, and cost-aware performance using modern tooling such as LangChain, Hugging Face, and LangGraph. By the end, you’ll be able to design production-ready LLM pipelines that combine prompting, adaptation, and retrieval for real-world use cases.
Price shown by Coursera — confirm on their site.
Enroll on CourseraYou'll be redirected to Coursera to complete enrollment.
- Listed & compared by CourseAsk
- English · All Levels
More courses from Coursera
Coursera
TCP/IP and Internet
Birla Institute of Technology & Science, Pilani · MOOC / Non-credit
Coursera
Agile Project Management
University of Colorado Boulder · Master's Degree
Coursera
Conservation and Sustainable Development
University of Michigan · MOOC / Non-credit
Coursera
Extra-Galactic Astronomy
University of Cambridge · MOOC / Non-credit