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CODeepLearning.AI· on Coursera
Generative AI for Software Development Specialization
4.6(3,200)·85K enrolled
Intermediate 40 hours English Professional CertificateFREE
About this course
This specialization picks up where general GenAI awareness courses leave off — it's built for engineers who want to actually ship LLM-powered features, not just understand the concepts. Across several courses, you work through prompt engineering patterns, retrieval-augmented generation (RAG), fine-tuning open models, evaluation strategies, and the engineering tradeoffs of deploying generative AI in production software.
DeepLearning.AI built its reputation on the original Deep Learning Specialization with Andrew Ng, and this program carries the same rigor — code-first labs over slide-heavy theory, taught by practitioners who've shipped GenAI features at scale. It assumes you can already code in Python; it does not assume deep ML theory background, making it accessible to software engineers pivoting into AI-enabled product work rather than ML researchers.
What you'll learn
Design effective prompts for production LLM applications
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Instructor
DT
DeepLearning.AI Team
Coursera instructor
85K+ learners9 courses4.6 instructor rating
Taught by DeepLearning.AI's instructor team, the organization founded by Andrew Ng that has trained millions of learners through its Deep Learning and Machine Learning Specializations on Coursera.
Requirements
Working proficiency in Python
Basic familiarity with APIs and software engineering concepts
No prior ML theory background required
Who this course is for
Software engineers who want to build LLM-powered features, not just use ChatGPT
ML engineers moving from traditional ML into generative AI
Product engineers tasked with adding AI features to existing software
No. It's designed for software engineers, not ML researchers. You need solid Python skills, but the specialization explains the AI concepts as it goes.
Yes — DeepLearning.AI also offers free, shorter standalone courses on specific topics (prompting, LangChain, etc). This specialization is the structured, multi-course program that ties those skills into building and deploying complete applications.
Graduates typically come away able to build RAG-based applications, fine-tune models for narrow tasks, and make informed architecture decisions about when to use which LLM technique in production software.
DeepLearning.AI certificates are well-regarded in the AI/ML hiring community given Andrew Ng's standing, though as with any certificate, it's strongest combined with a portfolio of shipped projects.