Engineer reliable GenAI systems
Design production-minded LLM applications with retrieval, tool use, agent workflows, MCP, evaluation, guardrails, and observability. Learn to optimise for quality, latency, safety, and cost.
Prerequisites: AI Branches plus basic Python or TypeScript
Developers and technical practitioners building AI applications
Neural networks explained simply — biological vs artificial neurons, layers, training, backpropagation, activation functions, and the difference between CNNs, RNNs, and Transformers. No maths required.
⏱️ 4 min readLearn prompt engineering from scratch — what it is, why it matters, 5 key techniques with practical examples, common mistakes to avoid, and the best tools to practise with.
⏱️ 4 min readGenerative AI explained simply — what it is, how it works (LLMs, diffusion models, GANs), real-world examples like ChatGPT and DALL-E, use cases, limitations, and how to learn it free.
⏱️ 4 min readAI Canopy is an intermediate-level program. We recommend completing AI Branches first, as Canopy dives deeper into neural networks, large language models, and AI system design.
Yes! AI Canopy covers how large language models work, including the transformer architecture that powers ChatGPT, Claude, Gemini, and similar models. You'll also master prompt engineering techniques.
No programming is required, though having basic technical literacy helps. We explain neural networks and LLMs conceptually with diagrams and interactive visualisations.
Yes! A personalised certificate is available upon completion of all lessons. It demonstrates your understanding of advanced AI concepts.
Prompt engineering is the skill of writing effective instructions for AI models like ChatGPT. It's one of the most in-demand skills in the AI era, and AI Canopy dedicates multiple lessons to mastering it.
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Understand the three major deep learning architectures and why transformers came to dominate modern AI.