Data Science & AIAI EngineeringIntermediateFeaturedTrending
Building Applications with Large Language Models
Retrieval, evaluation and cost control for LLM products that hold up.
4.9(396 ratings) 54 lessons · 7 modules 19h2.2k students
PN
What you'll learn
- Build retrieval-augmented generation that cites its sources
- Evaluate model output systematically instead of by vibes
- Cut token cost without losing quality
- Design prompt and model versioning you can roll back
About this course
Everyone can call an API; few can ship a reliable product on top of one. This course covers retrieval-augmented generation, prompt versioning, evaluation harnesses, caching, and cost control — the engineering between a demo and something you can charge for.
Curriculum
7 modules · 54 lessons · 19h
The detailed curriculum for this course is being finalised. The outline covers 54 lessons across 7 modules.
Requirements
- Comfortable with Python
- Basic API knowledge
Who this course is for
- Backend engineers building AI features
- Product engineers adding AI surfaces
Student reviews
No written reviews yet. The course has 396 ratings with an average of 4.9 out of 5.
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