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What are the best AI engineering courses for developers?

Short answer from Learnetto's Best AI engineering courses for developers guide.

Short answer

Use framework courses for implementation reps, then add production-focused resources from Chip Huyen, Full Stack Deep Learning, and eval or observability material. You need both code and operating judgement.

Context from the full guide

Use AI Hero or LangChain courses for app-building practice, Chip Huyen and Full Stack Deep Learning for production judgment, and eval/observability resources once your demo needs to survive real users.

Read the full guide

Useful resources

  1. AI SDK v6 Crash Course

    Workshop · Matt Pocock · Intermediate

    You want a structured AI SDK v6 course that covers model choice, text and object generation, UI streams, agents, persistence, context engineering, evals, and advanced app patterns.

  2. The AI Engineer Roadmap

    Free tutorial · Matt Pocock · Beginner to intermediate

    You want a guided path through core AI concepts, model selection, the AI engineering mindset, evals, and techniques for improving LLM-powered apps.

  3. LangChain for LLM Application Development

    Short course · DeepLearning.AI · Beginner to intermediate

    You want a fast introduction to building LLM applications with chains, retrieval, and tools.

  4. AI Engineering

    Book · Chip Huyen · Intermediate to advanced

    You are moving from demos to production systems.

  5. OpenAI Cookbook

    GitHub repo · OpenAI · Beginner to advanced

    You need implementation examples rather than theory.

  6. Microsoft AI Agents for Beginners

    GitHub repo · Microsoft · Beginner to intermediate

    You want a structured agent learning path with code.

  7. Prompt Engineering Guide

    Guide · DAIR.AI · Beginner to advanced

    You want examples of prompting techniques and patterns.

  8. LLM Fundamentals

    Free tutorial · Matt Pocock · Beginner

    You need clear mental models for system prompts, tokens, context windows, tools, and agents before building or using AI systems seriously.

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