Generative AI Engineer Professional Certificate

Go from understanding generative AI to designing, grounding, evaluating and securing applications built on large language models.

Understand how generative AI works and how to prompt it well, then design LLM applications: assembling prompts, reasoning, tool use and workflows. Learn how foundation models are built and evaluated, search your own documents with embeddings and vector databases, and build retrieval-augmented generation and agents. You finish with the design patterns, fine-tuning, evaluation and security that production applications need. Every course has hands-on labs and a capstone project your instructors review.

Prepares you for

AI engineer, LLM application developer, Generative AI developer, Prompt engineer

10 courses in this Professional Certificate

  • Generative AI Fundamentals: Understand how generative AI creates text and images, what it does well and badly, and how to check it and use it honestly. (Beginner, about 8 hours)
  • Prompt Engineering: From Basics to Advanced Techniques: Write prompts that get reliable results: clear instructions, examples, roles, structured output, reasoning, long documents, testing and reusable templates. (Beginner, about 9.5 hours)
  • Prompting and LLM Application Design: Design LLM features that hold up in production: structured outputs, reasoning, retrieval, tools, guardrails and evaluation, built in Python on the Claude API. (Intermediate, about 20 hours)
  • Engineering with Foundation Models: Choose, understand, evaluate and steer foundation models like an engineer: scale and serving arithmetic, transformer internals, rigorous model and system evaluation, LLM judges in CI, and prompt optimisation. (Advanced, about 20 hours)
  • Vector Databases and Semantic Search: Build semantic search that's fast and measurably right: embeddings, exact and approximate indexes, compression, hybrid ranking, filtering and vector databases. (Intermediate, about 18 hours)
  • Building RAG and Agentic Systems: Build retrieval-augmented and agentic AI systems that hold up: measured retrieval, hybrid search and re-ranking, tool-using agents with memory and plans, and multi-agent teams that check each other's work. (Advanced, about 22 hours)
  • Generative AI Design Patterns: Build robust GenAI applications from proven patterns: structured output, RAG, reasoning, agents with tools and hand-offs, guardrails and evaluation, each built, measured and its trade-offs known. (Advanced, about 29 hours)
  • Fine-Tuning Open Language Models with LoRA: Decide when fine-tuning pays, prepare chat data properly, and fine-tune open models with LoRA and QLoRA, from the maths and a LoRA layer of your own to transformers, peft, evaluation and serving. (Advanced, about 19 hours)
  • LLMOps: Evaluating, Tracing and Monitoring LLM Applications: Run LLM features like production software: golden sets and judges, regression gates with replayed recordings, OpenTelemetry traces, cost and guardrail metrics, and A/B tests. (Advanced, about 17 hours)
  • Securing LLM Applications: Protect your own LLM features: threat-model them with the OWASP Top 10, contain prompt injection in layers, handle output and tools safely, keep data private, and prove it with an automated test suite. (Intermediate, about 22 hours)