You’ll be able to design prompts and evaluation pipelines, fine-tune models efficiently, optimize inference performance, and build production-style RAG systems with grounding and measurable quality.

No. The specialization is designed to work with a mix of open-source models and optional API-based models. Wherever paid APIs are used, low-cost or free alternatives and clear cost controls are used for demonstration purposes.

Yes. Beyond theory, you’ll learn deployment, monitoring, cost optimization, and evaluation strategies for LLM systems, including production-style RAG pipelines and model lifecycle management.

This specialization focuses on LLM engineering, not just usage. You’ll work with prompt systems, fine-tuning workflows, retrieval pipelines, and evaluation frameworks, emphasizing reliability, scalability, and measurable performance.

Yes. The curriculum is aligned with LLM Engineer / Applied AI Engineer roles, helping you build practical skills in prompting, fine-tuning, optimization, and RAG that are commonly required in real-world AI teams.

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.