Generative AI has moved from demo to production requirement across engineering teams. This course focuses on the practical engineering skills — not just prompting — needed to ship reliable LLM-powered applications.

What You’ll Learn

LLM APIs and integration patterns
Prompt engineering for reliability and cost control
Retrieval-augmented generation (RAG) architecture
Evaluating and testing generative AI outputs
Responsible AI considerations in production systems

Curriculum

LLM Fundamentals & API Integration
Prompt Engineering Patterns
Embeddings & Vector Search
Building a RAG Pipeline
Function Calling & Structured Outputs
Evaluation & Testing LLM Applications
Cost & Latency Optimization
Capstone: Production-Style GenAI Application

Who Should Attend

Software engineers and technical leads building AI-powered features.

Prerequisites

Working knowledge of Python; basic API/REST concepts.

Hands-on Labs

Practice what you learn.

Hands-on lab availability is shown in the course details and enrolment information.

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Related Courses

Keep going after this course.

Part Of A Larger Path

This course is one stage in the AI & Machine Learning Learning Path

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Cohort dates, corporate training options, or how this fits your learning path.

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