Generative AI with Large Language Models

DeepLearning.AI follows the lifecycle of a generative application from data and model selection to evaluation and deployment. Its published description also covers transformer architecture, adaptation through fine-tuning and scaling decisions. The course connects technical model choices with practical compute and inference requirements.

What you will learn

• Explain the main stages of an LLM application lifecycle.
• Describe transformer architecture and model adaptation.
• Compare training, evaluation and deployment trade-offs.

Who this suits

Intermediate learners. Detailed prerequisites are not listed in the structured course facts; preview the expected Python and machine-learning background.

Before you enroll

Check the lab environment and model access supplied with enrollment. Fine-tuning and deployment decisions depend on data rights, compute resources and a clearly defined evaluation task.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Write a model-selection plan for one narrow application. Compare prompting and fine-tuning, then specify what evidence would justify the more expensive approach.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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