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How can I structure my AI learning path effectively?

Short answer from Learnetto's Best AI educators for beginners guide.

Short answer

Start with AI foundations, then choose one applied track such as coding, agents, RAG, product work, or automation. Finish that track with a small verifiable project before adding production concerns such as evals, observability, cost, security, and deployment.

Useful resources

  1. ChatGPT Prompt Engineering for Developers

    Short course · DeepLearning.AI · Beginner

    You want a short, structured intro to prompting for software tasks.

  2. Microsoft AI Agents for Beginners

    GitHub repo · Microsoft · Beginner to intermediate

    You want a structured agent learning path with code.

  3. Learn Prompting

    Guide · Sander Schulhoff · Beginner to intermediate

    You need a broad prompt engineering reference.

  4. Prompt Engineering Guide

    Guide · DAIR.AI · Beginner to advanced

    You want examples of prompting techniques and patterns.

  5. Beginner: Introduction to Generative AI

    Learning path · Google Cloud · Beginner

    Use this four-activity learning path for an introductory overview of generative AI, large language models, and responsible AI principles.

  6. Develop AI-Powered Prototypes in Google AI Studio

    Skill badge course · Google Cloud · Beginner

    Use this 45-minute hands-on course to practice prompting, multimodal image and video analysis, and rapid application prototyping in Google AI Studio.

  7. Prompting Claude Fable 5.1

    Prompting guide · Anthropic · Intermediate to advanced

    You are testing or migrating to Claude Fable 5.1 and want model-specific guidance for effort, progress updates, tool-call batching, long tasks, compaction, subagents, file edits, and verification.

  8. OpenAI Academy

    Official learning hub · OpenAI · Beginner to advanced

    You want OpenAI's public course hub and guided learning material rather than piecing together model docs, blog posts, and third-party tutorials.

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