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Beginner~8 hours8 lessons

AI Engineer Foundation

Core concepts every AI engineer needs before building production systems. Start here to build a solid mental model of embeddings, APIs, prompts, vectors, evaluation, and basic deployment.

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What you'll learn

Python for AI applications
APIs and JSON for LLM integrations
Embeddings and semantic search
Vector search fundamentals
Prompt engineering
Structured outputs and function calling
Basic evaluation frameworks
Deployment basics with Docker and cloud

Learning outcomes

1Build confidence reading and writing AI engineering code
2Understand embedding models and how to use them
3Write reliable prompts with structured outputs
4Deploy a simple AI application end to end
5Evaluate LLM outputs with basic metrics

Related lessons

AI Engineering Roadmap

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Agentic AI Fundamentals

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RAG Systems Explained

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LLMOps Basics

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Prompt Engineering for Production

7 min

Vector Databases Explained

7 min

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Practice quizzes

Ai Engineering quizPrompt Engineering quizVector Databases quiz

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Build, evaluate, debug, and deploy real-world retrieval-augmented generation systems. Go beyond toy examples to handle chunking tradeoffs, hybrid retrieval, reranking, citation grounding, and RAG observability.

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Intermediate
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Agentic AI Engineer

Design reliable AI agents with tools, memory, planning, guardrails, and human review workflows. Learn the patterns used in production agentic systems, not just demos.

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Tool calling and function designAgent loop architecturePlanner-executor-verifier patternLangGraph-style state machines+9 more
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