AI learning guide

Best AI educators for developers

Build LLM apps, agents, RAG systems, local model tools, and AI workflows.

Quick answer

Best first move

Pick one educator from the table, watch one matched video, then open one hands-on resource. The aim is to leave with a working habit, project, or mental model tied to developers.

How to shortlist

Compare audience, level, format, and starting point. Favor a source that matches your current role and gives you exercises, examples, docs, or code you can use immediately.

What to avoid

Avoid collecting dozens of generic AI tips. Use this guide to choose a narrow learning loop: one topic, one educator, one video, one resource, one application in your own work.

Videos to watch

Prompt engineering for developers

DeepLearning.AI

Agents for everything else

AI Engineer

Machine learning crash course

Google for Developers

Neural networks from scratch

Andrej Karpathy

LLM evaluation with W&B

Weights & Biases

2025 in LLMs so far

Simon Willison

AI Engineering with Chip Huyen

Chip Huyen

Educators and sources

Educator / source Best for Skills Start with
Developers, AI engineers AI engineering, Agents, Developer tools Watch AI Engineer talks for production patterns and tool choices.
Everyone from beginners to builders Prompting, Agents, RAG, ML foundations Start with ChatGPT Prompt Engineering for Developers, then pick a RAG or agents course.
Developers, data scientists Practical ML, Ethics, Education Use fast.ai essays and course material alongside hands-on notebooks.
Developers, technical generalists LLM tools, Prompting, AI safety, Local models Read recent LLM posts and try the llm command-line tool.
Developers building LLM apps Structured outputs, Extraction, RAG Try the Instructor examples for extraction and validation.
Builders shipping LLM systems Evals, RAG, LLM product quality Read the evals guide and build a small test set for your own app.
Engineers, ML practitioners AI engineering, Systems, Production ML Use the book page and related essays as a production engineering path.
Developers, researchers Prompting, RAG, Reasoning, Agents Use the prompting techniques and RAG sections as a reference.
Visual learners, developers Transformers, Embeddings, LLM concepts Read The Illustrated Transformer, then The Illustrated GPT-2.
Developers, engineering leaders AI coding, Engineering workflows, Frontend Look for AI coding and engineering workflow posts.
AI engineers, founders, researchers AI engineering, Industry context, Model ecosystem Pick interviews with engineers building tools you already use.
Developers and ML learners Agents, Transformers, Diffusion, Open models Take the Agents Course if you want agent basics, or the NLP/Transformers course if you need model fundamentals.
Beginners learning ML foundations ML foundations, Classification, Embeddings, Neural networks Start the Machine Learning Crash Course and finish the exercises rather than only watching videos.
Developers building agents and LLM apps LangGraph, Agents, LLM orchestration, RAG Start with Introduction to LangGraph and work through the notebooks.
Developers building RAG and document agents RAG, Agents, Document workflows, Context augmentation Read the LlamaIndex introduction, then build a small document Q&A app.
Developers evaluating and deploying LLM apps LLM apps, Evals, Experiment tracking, MLOps Take Building LLM-powered apps, then the evaluation material.
Developers building with OpenAI APIs API examples, RAG, Agents, Structured outputs Search the cookbook for the exact pattern you need and run the notebook locally.
Developers learning agents from concept to code Agents, Multi-agent workflows, Tool use Start with lesson 1 on what agents are, then build through the GitHub lessons.

Resources

Neural Networks: Zero to Hero

Video course · Andrej Karpathy · Intermediate

You want to understand neural networks and language models from code.

AI Engineering

Book · Chip Huyen · Intermediate to advanced

You are moving from demos to production systems.

LLM Evals

Guide · Hamel Husain · Intermediate

Your AI app needs quality checks before users see it.

AI Engineer Talks

Conference videos · AI Engineer · Intermediate to advanced

You want practitioner talks from builders working on the current AI stack.

AI Engineer

Conference talks · Swyx · Intermediate to advanced

Use this when you want Swyx's material for ai engineering and related AI skills.

AI Engineering

Book · Chip Huyen · Intermediate to advanced

Use this when you want Chip Huyen's material for ai engineering and related AI skills.

AI Engineering resources

Newsletter · Addy Osmani · Beginner to intermediate

Use this when you want Addy Osmani's material for ai coding and related AI skills.

Latent Space

Podcast · Latent Space · Intermediate to advanced

Use this when you want Latent Space's material for ai engineering and related AI skills.

Machine Learning Crash Course

Course · Google for Developers · Beginner

Use this when you want Google for Developers's material for ml foundations and related AI skills.

W&B Courses

Free courses · Weights & Biases · Intermediate

Use this when you want Weights & Biases's material for llm apps and related AI skills.

Python Engineer

YouTube tutorials · Patrick Loeber · Beginner to intermediate

Use this when you want Patrick Loeber's material for pytorch and related AI skills.

Questions this guide answers

Which AI educators are best for software developers?

Use the educator table, videos, and resources above to compare options by topic fit, depth, and format. A good choice gives you a concrete next step: a course module, code example, video walkthrough, project, or workflow you can try today.

How do I move from API demos to useful AI apps?

Use the educator table, videos, and resources above to compare options by topic fit, depth, and format. A good choice gives you a concrete next step: a course module, code example, video walkthrough, project, or workflow you can try today.

Which resources teach coding with AI and LLM app architecture?

Use the educator table, videos, and resources above to compare options by topic fit, depth, and format. A good choice gives you a concrete next step: a course module, code example, video walkthrough, project, or workflow you can try today.