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AI Engineering

The full professional track for building AI products for production — from a first prompt to a stable, safe and measurable system. Organized into 3 levels: beginner, advanced and expert.

AI Engineering is the field of building real products on top of language models — not just "talking to ChatGPT," but designing, building, evaluating and maintaining AI systems that run in production for real users. It's a blend of software engineering, working with LLMs, and infrastructure. This track takes you step by step — pick your level and start.

1

Beginner — the fundamentals

Understand how an LLM works and build your first app

arrow_backWhat is AI Engineering — a roadmap arrow_backPrompt Engineering arrow_backStructured Outputs & Tool Use arrow_backWorking with an API (Claude) arrow_backLangChain — a framework arrow_backPrompt library
2

Advanced — real systems

RAG, agents, evals — turning a prototype into a product

arrow_backRAG — answers from your data arrow_backEmbeddings — semantic vectors arrow_backVector Databases arrow_backContext Engineering arrow_backEvals — measuring quality arrow_backTool Use — tools for agents arrow_backAgent Frameworks arrow_backMCP — connecting tools arrow_backHallucinations — reducing them arrow_backReranking — more accurate RAG arrow_backMultimodal — images, documents, audio arrow_backAgent Memory arrow_backAdvanced RAG
3

Expert — production, safety & LLMOps

Guardrails, security, monitoring, cost and fine-tuning

arrow_backGuardrails — safety rails arrow_backLLM security arrow_backPrompt Injection arrow_backAI agent security arrow_backAI Observability — monitoring arrow_backLLMOps — running in production arrow_backLLM Caching — cheaper & faster arrow_backCutting LLM costs arrow_backFine-tuning — custom training
smart_toyAI Agents Hub tagLLM Hub schoolLearning tracks

Starting from scratch?

Start with the "What is AI Engineering" guide — it explains the full picture and the right learning order.

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