LLM Engineering: Prompting, Fine-Tuning, Optimization & RAG

Coursera MOOC / Non-credit USD 49
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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.

What you'll learn

  • create effective prompts for LLMs
  • implement fine-tuning workflows
  • optimize LLM models for performance
  • integrate retrieval-augmented generation techniques
  • build LLM applications using LangChain and Hugging Face tools

Skills you'll gain

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