Prompt engineering for developers
DeepLearning.AI · prompting, api, developers
Learnetto AI directory
Find the best AI educators, resources, and learning paths.
A compact directory for people who want practical AI skills and up-to-date official docs: prompting, automation, coding agents, MCP, model selection, AI product work, RAG, evals, model internals, and production AI engineering.
57 educators
12 learning paths
143 resources
Watch first
Fast entry points for prompting, coding agents, model selection, RAG, evals, and production AI.
Claude Code beginner's tutorial
Peter Yang · coding agents, claude code, coding
Agents for everything else
AI Engineer · agents, ai engineering, developer tools
LangGraph introduction
LangChain · agents, langgraph, llm orchestration
Hugging Face agents course
Hugging Face · agents, open models, tools
Machine learning crash course
Google for Developers · ml foundations, classification, neural networks
Educators to start with
Sorted for range: founders, product teams, developers, evals, model internals, and production systems.
| Educator | Best for | Skills | Start with |
|---|---|---|---|
| Indie founders, product builders | Founder workflows, Product ops, Automation | Read the public notes and examples before deciding whether the paid material matches your business. | |
| Founders, service business owners | Founder workflows, Systems, Automation | Look for workflow breakdowns and implementation examples. | |
| Product managers, founders | Product strategy, Writing, AI adoption | Review the Maven syllabus and compare it to your current product workflow. | |
| Product teams, founders | Product, Growth, Team adoption | Browse the How I AI interviews and copy the workflows that match your role. | |
| Knowledge workers, educators, leaders | AI literacy, Workplace adoption, Strategy | Read recent essays on using AI as a collaborator and on organizational adoption. | |
| 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. | |
| Coders learning deep learning | Deep learning, PyTorch, Model training | Practical Deep Learning for Coders, lesson 1. | |
| Developers, data scientists | Practical ML, Ethics, Education | Use fast.ai essays and course material alongside hands-on notebooks. | |
| Programmers who want model internals | Neural networks, Backprop, LLM internals | Watch micrograd, then makemore, then the GPT video. | |
| Developers, technical generalists | LLM tools, Prompting, AI safety, Local models, Model selection | Read the recent model-roundup posts, then try the llm command-line tool with two or three different providers. | |
| Developers building LLM apps | Structured outputs, Extraction, RAG | Try the Instructor examples for extraction and validation. |
Choose by skill
Use these as quick routes into the directory.
AI foundations
Useful for: Everyone
Learn: What LLMs can and cannot do; Tokens, context windows, hallucinations; Privacy and data handling
Resources: DeepLearning.AI, Learn Prompting, Ethan Mollick
Prompting and writing
Useful for: Writers, operators, PMs, founders
Learn: Clear task framing; Examples and constraints; Editing, synthesis, and critique loops
Resources: Learn Prompting, DAIR.AI, Peter Yang
Automation and agents
Useful for: Founders, operations teams, developers
Learn: Trigger-based workflows; Tool calling; Agent failure modes; Human review points
Resources: Brian Casel, Josh Pigford, AI Engineer
AI product work
Useful for: PMs, designers, founders
Learn: AI UX patterns; Use-case selection; Prototyping; Measuring quality
Resources: Peter Yang, Lenny Rachitsky, Shreya Shankar
Coding with AI
Useful for: Software developers
Learn: Codebase navigation; Test generation; Refactoring; Reviewing AI output
Resources: Simon Willison, Addy Osmani, AI Engineer
Context engineering and MCP
Useful for: Developers using coding agents and tool-connected workflows
Learn: How to structure instructions, memories, and skills; When to use MCP servers versus retrieval or file search; Subagents, hooks, and tool boundaries; How to keep agent context small, relevant, and testable
Resources: Anthropic, Hugging Face, OpenAI, Simon Willison
RAG and knowledge systems
Useful for: Developers building AI apps
Learn: Chunking and retrieval; Structured extraction; Reranking; Grounded answers
Resources: Jason Liu, Hamel Husain, Eugene Yan
Evals and reliability
Useful for: AI product teams
Learn: Test sets; Human review; Regression checks; Quality metrics
Resources: Hamel Husain, Shreya Shankar, Chip Huyen
Resources worth opening
Courses, guides, repos, books, model catalogs, and official docs with a practical reason to use each one.
| Resource | Type | Level | Use when |
|---|---|---|---|
|
Neural Networks: Zero to Hero
Andrej Karpathy
|
Video course | Intermediate | You want to understand neural networks and language models from code. |
|
Practical Deep Learning for Coders
fast.ai
|
Free course | Intermediate | You can code and want a practical route into training models. |
|
ChatGPT Prompt Engineering for Developers
DeepLearning.AI
|
Short course | Beginner | You want a short, structured intro to prompting for software tasks. |
|
OpenAI Cookbook
OpenAI
|
GitHub repo | Beginner to advanced | You need implementation examples rather than theory. |
|
Microsoft AI Agents for Beginners
Microsoft
|
GitHub repo | Beginner to intermediate | You want a structured agent learning path with code. |
|
Build a Large Language Model From Scratch
Sebastian Raschka
|
GitHub repo / book | Intermediate | You want to build an LLM step by step. |
|
The Illustrated Transformer
Jay Alammar
|
Visual guide | Beginner to intermediate | Transformer architecture still feels fuzzy. |
|
Learn Prompting
Sander Schulhoff
|
Guide | Beginner to intermediate | You need a broad prompt engineering reference. |