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Generative AI & Prompt Engineering

Build with large language models like a professional — prompting patterns, RAG, fine-tuning, evaluation, and production GenAI architecture.

Intermediate0.9 hours8 lessons
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What You'll Learn

  • ✓Understand the GenAI landscape in 2026: foundation models, multimodal, open-weight vs closed
  • ✓Explain how LLMs work — tokens, context windows, sampling, temperature
  • ✓Apply core prompt engineering patterns: role, examples, structure, constraints
  • ✓Use advanced techniques: chain-of-thought, ReAct, function calling, tool use
  • ✓Design and operate Retrieval-Augmented Generation (RAG) systems
  • ✓Decide when to fine-tune, when to RAG, and when prompt engineering is enough
  • ✓Evaluate LLM outputs and manage hallucination, bias, and safety
  • ✓Architect production GenAI applications with caching, observability, and cost control

Prerequisites

  • •Comfort with APIs and at least one programming language (Python preferred for examples)
  • •No prior ML experience required — concepts are built up from first principles

Course Curriculum

Module 1: Foundations

  1. 01The GenAI Landscape in 20266 min
  2. 02How LLMs Actually Work (Tokens, Context, Sampling)7 min

Module 2: Prompting

  1. 01Prompt Engineering Patterns7 min
  2. 02Advanced Prompting: Chain-of-Thought, ReAct, and Tools7 min

Module 3: Grounding and Customisation

  1. 01Retrieval-Augmented Generation (RAG)8 min
  2. 02Fine-Tuning vs RAG vs Prompt Engineering6 min

Module 4: Production

  1. 01Evaluation, Hallucination, and Safety7 min
  2. 02Building Production GenAI Applications7 min

Practice for the Real Exam

After completing this course, test yourself with exam-style practice questions.

Practice AIF C01 QuestionsPractice AI 102 Questions