Learn AI Engineering

Start AI Engineering in 2026 - Build real AI systems, mostly for free!

A complete guide to start and improve in AI engineering in 2026 without ANY background in the field and stay up-to-date with the latest news, tools, and state-of-the-art techniques.

We now have a full suite of AI engineering courses with the one you need, from beginner Python to advanced agentic systems. Check them out here: Towards AI Academy.

I am asked the same question all the time on social media, in the community, in courses, and even when we hire AI engineers for consulting work at Towards AI:

"How can I become an AI engineer?"

Sometimes it sounds like "How do I start if I have no background?", "Do I need machine learning first?", "Should I learn RAG, agents, fine-tuning, or prompting?", "Can I just use Codex or Claude Code and skip the boring parts?", or "What should I actually build to get hired?"

So I decided to make a complete guide for anyone who wants to start learning AI engineering in 2026 from little or no background, mostly for free.

This guide is the AI engineering version of my older Start Machine Learning repo, but updated for what the field actually looks like now.

And the field changed a lot.

A few years ago, many companies mostly asked: "Can we train a model for this?" or "Can we fine-tune a machine learning model on our data?"

Now, for a huge number of teams, the model already exists. GPT is there. Claude is there. Gemini is there. Llama, Mistral, Qwen, DeepSeek, and many others are there. The new question is: what do we build around these models so they become reliable enough to use?

That is AI engineering.

It is not just prompting. It is not just building chatbots. And it is definitely not only opening an agentic coding tool and asking it to vibe code ten demos.

AI engineering is the applied skill of taking foundation models and turning them into useful, reliable systems. It is product thinking, data, context, tools, retrieval, evals, deployment, monitoring, safety, cost, and user feedback all living together in one system.

Coding agents like Codex, Claude Code, Cursor, Gemini CLI, and similar tools are amazing. You should use them. I use them too. But using an agent to build faster is not the same as understanding what you built.

The valuable skill companies pay for is judgment: deciding what should be built, choosing between prompting, RAG, fine-tuning, workflows, agents, or no LLM at all, proving that the system works, and improving it when reality disagrees.

That is the real focus of this guide.

Find the complete and always-updated list on GitHub:

GitHub - louisfb01/start-ai-engineering

Maintainer: Louis-François Bouchard, also active on YouTube, X, LinkedIn, the What's AI Podcast, and my newsletter.

Tag me on X @Whats_AI or LinkedIn if you share the list.

Resources marked paid are clearly labelled. Almost everything else is free. Some course and book links are affiliate links that support this guide at no extra cost to you. Thank you if you use them, and have fun learning.

Don't be afraid to replay videos, reread articles, or build the same idea twice. Repetition, debugging, and broken projects are where the real learning happens.

Who can become an AI engineer in 2026?

This guide is intended for anyone with zero or a small background in programming, artificial intelligence, or machine learning.

There is no single correct order to follow, but a classic path would be from top to bottom. If you do not like books, skip the book section. If you do not want to follow an online course, skip courses and build from docs. If you already know Python, jump straight into the AI engineering parts.

The goal is not to force one perfect path. The goal is to give you a clear map when everything online feels noisy.

If you are advanced, use this as a checklist. If you are a beginner, follow the order and keep the difficulty levels in mind.

Difficulty levels

Each resource has a difficulty marker from 1️⃣ to 🔟.

1️⃣ means absolute beginner, like an intro Python resource. 3️⃣ is beginner-friendly AI vocabulary. 5️⃣ is practical builder material you can apply in a project. 7️⃣ is production engineering depth. 9️⃣ is advanced systems or research. 🔟 is the kind of senior-level paper or technique you should probably revisit after you have shipped a few systems and suffered a little. In a good way. Mostly.

The learning path I would follow

If I were starting from scratch today, this is the rough path I would take:

1. Watch a few foundational videos to pick up vocabulary and intuition.
2. Pick one free course and one framework whose docs you commit to reading end to end.
3. Pick one or two books to build a solid foundation you can return to when the tools change.
4. Optionally take one or two advanced applied courses with real projects, especially if you want structure and feedback before breaking things on your own.
5. Build two or three small but real projects that break in interesting ways.
6. Add evaluations, tracing, logging, cost tracking, and deployment before you call anything production-ready.

After that, you should have the foundations of a solid AI engineer ready for many entry-level or transition roles. Most importantly, keep learning and keep an open mind. This field changes fast, and the best AI engineers stay curious instead of getting religious about one model, one framework, or one workflow.

You can use AI to personalize this roadmap

You can also feed the full GitHub repo to your favorite AI agent and ask it to create a plan based on your background, time, budget, and preferred learning style.

Paste this into Claude, ChatGPT, Codex, Cursor, or another assistant:

Use this repo as my AI engineering roadmap: https://github.com/louisfb01/start-ai-engineering

Create a personalized learning plan for me. First ask about my background, coding level, available time, budget, preferred learning style, and goals. Then choose the most relevant resources from the repo, explain why you picked them, order them from easiest to hardest, and turn them into a weekly plan with projects, checkpoints, and what I should be able to build after each stage.

Use AI to learn AI engineering faster, not to avoid learning.

Table of contents

Here is a quick table of contents if you want to skip ahead:

• Let's dive in! Start with short YouTube videos
• No coding background, no problem
• Start learning seriously with courses and docs
• Read articles online
• Read important books
• Practice, practice, and practice
• Prompting and structured outputs
• RAG, context engineering, and vector databases
• Tools, MCP, workflows, and agents
• Evaluations, observability, and harnesses
• Fine-tuning, multimodal, voice, and deployment
• AI coding agents and developer tools
• AI safety, security, and guardrails
• More resources: communities, newsletters, podcasts, and people to follow
• How to find an AI engineering job
• Conclusion

Let's dive in and start learning AI engineering!

In my opinion, the best way to start learning anything technical is with short videos that build vocabulary and intuition.

Do not start by trying to memorize every paper, every framework, or every model release. Start by understanding what people mean when they say transformer, token, embedding, context window, RAG, reranker, tool call, agent, eval, and hallucination.

Here are the videos I would start with.

• 4️⃣ AI Engineering Foundations: What Developers Actually Need to Know Today - My one-hour AI engineering foundations workshop. Start here if you want the cleanest overview of what this guide is about: how LLMs work, their limitations, when to use prompting, RAG, workflows, or agents, and why evaluations and security matter before production.
• 2️⃣ How AI Works in Super Simple Terms - StatQuest with Josh Starmer. The gentlest possible on-ramp if you are brand new.
• 2️⃣ Mastering AI Jargon - Your Guide to OpenAI & LLM Terms - My practical glossary for the terms you keep seeing around OpenAI, GPT, LLMs, prompting, and generative AI.
• 3️⃣ Intro to Large Language Models - Andrej Karpathy. One hour. Still one of the cleanest high-level tours of what an LLM is and how it works.
• 4️⃣ AI Fundamentals for Builders - Understand transformers and fix LLM limitations - A builder-focused session on transformer intuition, common LLM limitations, and the techniques used to work around them.
• 5️⃣ A Hackers' Guide to Language Models - Jeremy Howard, fast.ai. Practical, code-friendly, and builder-oriented.
• 6️⃣ Deep Dive into LLMs like ChatGPT - Andrej Karpathy. Longer, deeper, and probably one of the best free investments you can make this year.

Great YouTube channels to subscribe to:

• 2️⃣ StatQuest with Josh Starmer - The clearest visual explanations of ML and neural network concepts.
• 3️⃣ 3Blue1Brown - Visual math and deep learning intuition. The neural networks and attention series are especially useful.
• 3️⃣ DeepLearning.AI - Free short courses and practical AI engineering topics from Andrew Ng's team.
• 4️⃣ What's AI - My channel for practical AI engineering explainers on RAG, agents, MCP, evals, and how to reason about the stack.
• 4️⃣ Hugging Face - Official tutorials across the open-source AI ecosystem.
• 5️⃣ LangChain - Official tutorials on LangChain, LangGraph, agents, and workflows.
• 6️⃣ Andrej Karpathy - Long-form explanations of how LLMs actually work.
• 7️⃣ Umar Jamil - Line-by-line implementations for people who want to understand what is happening inside the model.
• 8️⃣ Yannic Kilcher - In-depth paper walkthroughs and research commentary.

Feel free to keep watching videos on YouTube. It is still one of the most underrated free learning platforms if you choose carefully.

No coding background, no problem!

If you have no coding background at all, start with Python.

You do not need to become a software engineering wizard before touching AI, but you do need enough Python to read docs, call APIs, manipulate data, debug errors, and understand what your coding agent generated.

Start here:

• 1️⃣ Learn Python - Free interactive Python tutorial.
• 1️⃣ AI Python for Beginners - DeepLearning.AI. Free, friendly, and lighter than a full bootcamp.
• 2️⃣ Python Fundamentals + CS Concepts - My playlist covering Python fundamentals and core computer science concepts.
• 2️⃣ Beginner Python for AI Engineering - Towards AI. Python taught as a practical tool for working with LLM systems. Paid.

If you already know basic Python, you can jump into the rest of the guide.

You do not need a mathematics PhD. You do not need to implement every model from scratch before building. But you do need enough technical comfort to understand the system you are shipping.

Start learning seriously

Once you have some vocabulary and Python basics, pick one structured path.

Do not try to take ten courses at the same time. Pick one course and one framework. Commit to reading the docs properly. Not just the quickstart. The docs.

Here are the courses and learning paths I would recommend first:

• 4️⃣ Hugging Face LLM Course - Free. The best free structured path through tokenization, fine-tuning, and modern transformers.
• 4️⃣ Anthropic Academy - Free. Includes an introduction to MCP and strong Claude-focused learning resources.
• 5️⃣ Hugging Face Agents Course - Free. Walks through agents, tools, and orchestration using open-source models.
• 5️⃣ LangChain Academy - Free. Official path through LangChain and LangGraph.
• 3️⃣ ChatGPT Prompt Engineering for Developers - DeepLearning.AI. Free and still a useful starting point.
• 4️⃣ Building Systems with the ChatGPT API - Free. Multi-step chains, moderation, and beginner-level evals.
• 5️⃣ Improving Accuracy of LLM Applications - Free. Practical methods for moving from "it kind of works" to something more reliable.

Towards AI Academy paths:

• 2️⃣ AI for Work - For non-developers who want to use AI at work. Paid.
• 3️⃣ 10-Hour LLM Fundamentals - Compact crash course on when to use prompting, RAG, fine-tuning, or agents. Paid.
• 4️⃣ Get it all! From Novice to Expert Bundle - Best option if you want the whole stack from nothing. Paid.
• 5️⃣ Full Stack AI Engineering - Our flagship developer path across prompting, RAG, fine-tuning, tools, agents, and deployment. Paid.
• 7️⃣ Agentic AI Engineering - Advanced course with two production agents as capstones. Paid.

If you want my simple recommendation: if you are new, start with a free course plus one framework's docs. If you want structure and projects, pick a Towards AI course that matches your level.

Read articles online

Reading matters because articles force you to slow down and understand decisions.

A good loop is: read one conceptual article, read one official docs page, build one tiny version yourself, then reread the article once you have scars. The second pass hits very differently.

Here are a few articles worth reading early:

• 4️⃣ Here's how I use LLMs to help me write code - Simon Willison's practical workflow for working with coding agents.
• 5️⃣ Your AI Product Needs Evals - Hamel Husain. The canonical starting point for why evals matter.
• 6️⃣ A Field Guide to Rapidly Improving AI Products - Hamel Husain. How to move from "it kind of works" to a real product.
• 6️⃣ Building Effective AI Agents - Anthropic. The reference post on when to use workflows and when autonomy actually pays off.
• 6️⃣ Harness Engineering: The Missing Layer Behind AI Agents - My article on the layer between prompt engineering and working agents: tools, permissions, state, retries, checkpoints, guardrails, and evals.
• 6️⃣ Agents - Chip Huyen's long-form primer on agent design.
• 7️⃣ Context Engineering for LLMs: Build Reliable, Production-Ready RAG Systems - Practical RAG and context engineering walkthrough.
• 7️⃣ 12-Factor Agents - Dex Horthy's production-agent checklist.
• 8️⃣ The Lethal Trifecta for AI Agents - Simon Willison on the security risk every agent builder needs to understand.

For ongoing reading, rotate between practitioner blogs, official engineering posts, the Towards AI publication on Medium, and the Towards AI Newsletter instead of relying on one source.

Read important books

Books are optional, but they are still one of the best ways to build foundations.

If you prefer reading to watching, this path goes very far, especially with books focused on actually coding and building.

You do not need to read all of these. Please do not turn learning into a book-hoarding side quest. Pick one or two based on your level and goals.

• 5️⃣ Building LLMs for Production - Towards AI. Covers prompting, RAG, fine-tuning, reliability, and shipping. The Academy e-book version is also available. Paid.
• 5️⃣ Hands-On Large Language Models - Jay Alammar and Maarten Grootendorst. Visual, code-first, and excellent for intuition. Paid.
• 5️⃣ Prompt Engineering for LLMs - John Berryman and Albert Ziegler. Field-tested prompting patterns from GitHub Copilot engineers. Paid.
• 6️⃣ The LLM Engineer's Handbook - Paul Iusztin and Maxime Labonne. Production-focused and built around a real end-to-end project. Paid.
• 7️⃣ AI Engineering - Chip Huyen. Strong on system design, evaluation, and when each technique earns its place. Paid.
• 8️⃣ Build a Large Language Model From Scratch - Sebastian Raschka. The right book if you want to understand tokenization, attention, transformer blocks, and fine-tuning from the inside. Paid.

Free long-form explainers worth bookmarking:

• 4️⃣ The Illustrated Transformer - Jay Alammar. The classic visual reference for transformer architecture.
• 5️⃣ Prompt Engineering - Lilian Weng. Research-oriented overview of prompting techniques.
• 5️⃣ Patterns for Building LLM-based Systems & Products - Eugene Yan. Seven patterns that almost every shipped LLM product ends up using.
• 7️⃣ LLM Powered Autonomous Agents - Lilian Weng. Still one of the reference posts on agents.
• 8️⃣ Why We Think - Lilian Weng on test-time compute and reasoning models.

Practice, practice, and practice!

Reading and watching will only take you so far.

You become an AI engineer by building systems that fail in educational ways.

Before you start building, I recommend watching this video:

In it, I share what I look for when hiring AI engineers, why decision-making matters more than polished agent-generated output, and what kinds of projects actually teach useful skills.

Good first projects:

• 4️⃣ A document question-answering assistant with citations and a real eval set.
• 4️⃣ A customer support workflow with tool calls and structured outputs.
• 5️⃣ A research assistant that plans, searches, reads, and writes a short brief.
• 5️⃣ A coding helper scoped to one narrow internal task.
• 5️⃣ A multimodal invoice or receipt parser with validation.
• 6️⃣ Designing Real-World AI Agents Workshop - Paul Iusztin's hands-on workshop for building a Deep Research Agent plus a LinkedIn Writing Workflow as MCP servers.
• 6️⃣ A small agent that plans, acts, checks, and retries within a budget.

Reference repos and tutorials:

• 3️⃣ OpenAI Cookbook - Official recipes in notebook form.
• 4️⃣ Google Gemini Cookbook - Google recipes for multimodal, long context, and tool use.
• 4️⃣ LlamaIndex Starter Tutorial - Fast path to a working RAG pipeline.
• 4️⃣ AI Engineering Cheatsheets - My decision tables and playbooks for choosing approaches.
• 5️⃣ Pydantic AI docs - Type-safe agent framework from the Pydantic team.
• 6️⃣ DSPy Tutorials - Prompt/program optimization tutorials.
• 7️⃣ Paul Iusztin's hands-on-llms repo - End-to-end production project with training, serving, and monitoring.

For every project, force yourself to answer:

• Why is this prompt, tool, or architecture chosen?
• Where and how will it fail?
• How will I evaluate it, offline and online?
• What will I log and inspect when it misbehaves?
• What is the cheapest design that still clears the bar?
• Is an agent actually the right choice here, or is a workflow enough?

If you cannot answer those, keep building.

Prompting and structured outputs

Prompting still matters in 2026.

But the useful version is not magic words or cute tricks. It is writing reliable contracts for non-deterministic systems.

You should learn task framing, output contracts, structured outputs, JSON schemas, few-shot examples, grounding, citations, verification loops, tool-use instructions, completion criteria, and prompt versioning.

Best resources:

• 3️⃣ OpenAI Prompt Engineering Guide - Official and API-centric.
• 3️⃣ Anthropic Prompt Engineering Overview - Claude-specific advice that generalizes well.
• 3️⃣ Learn Prompting - Free, community-maintained reference.
• 5️⃣ OpenAI GPT-5 Prompting Guide - Model-specific prompting advice from OpenAI.
• 5️⃣ Instructor: structured outputs with Pydantic - Jason Liu's library for typed LLM outputs.
• 6️⃣ Structured Data Extraction from Unstructured Content Using LLM Schemas - Simon Willison's schema-first extraction pattern.

Treat prompts as code you version, interfaces you test, and product decisions you revisit.

RAG, context engineering, and vector databases

RAG is still a core technique, but the naive "stuff some chunks into the prompt" version is no longer enough.

You should understand chunking, embeddings, vector search, hybrid search, BM25, reranking, citations, provenance, metadata filters, query rewriting, corrective RAG, retrieval evaluation, and when RAG is the wrong answer.

Start here:

• 4️⃣ Why RAG Is Not Training Your AI - My explanation of the mental model most builders get wrong.
• 4️⃣ LlamaIndex Introduction to RAG - Official docs and one of the cleanest free paths to a working RAG system.
• 4️⃣ Pinecone RAG guide - Vendor-written, but a solid introduction with diagrams.
• 5️⃣ Is RAG Still Needed in the Era of Long Context LLMs? - Useful decision framework.
• 6️⃣ Contextual Retrieval in AI Systems - Anthropic's contextual chunking pattern.
• 6️⃣ Hybrid Search RAG That Actually Works - BM25, vectors, and reranking in practice.
• 7️⃣ Systematically Improving RAG - Jason Liu's RAG iteration playbook.
• 8️⃣ Evolve or perish: The new RAG paradigm - Paul Iusztin on where RAG is heading.

Embeddings and vector databases:

• 4️⃣ Cohere Embed and Rerank
• 4️⃣ Voyage AI
• 4️⃣ Jina Embeddings
• 4️⃣ Nomic Embed
• 5️⃣ Hugging Face MTEB Leaderboard
• 4️⃣ Qdrant docs
• 4️⃣ Weaviate docs
• 4️⃣ LanceDB docs
• 4️⃣ Pinecone docs
• 4️⃣ pgvector
• 4️⃣ Chroma

Do not stop at "uploaded PDF, got answer." Build one serious RAG app with citations, retrieval debugging, chunking choices, metadata filters, an eval set, and a way to inspect misses.

Tools, MCP, workflows, and agents

If prompting was the first phase of AI apps, tools and agents are where real capability and real risk start showing up.

Tools let a model act. MCP helps connect models to external systems. Workflows define predictable steps. Agents decide steps dynamically.

Most teams should start with a workflow. Add autonomy only where it clearly buys something.

Tools and MCP:

• 5️⃣ Anthropic Tool use overview - Clean reference for tool calling.
• 5️⃣ Introducing the Model Context Protocol - Original MCP announcement.
• 5️⃣ Model Context Protocol Getting Started - Official MCP docs.
• 5️⃣ Hugging Face MCP Course - Free course covering MCP clients and servers.
• 6️⃣ Anthropic Skills - Reusable agent skills and instructions.
• 7️⃣ Writing effective tools for agents - Tool schemas, descriptions, and error handling.
• 8️⃣ Model Context Protocol has Prompt Injection Security Problems - Read before deploying MCP servers that touch private data.

Agents and workflows:

• 5️⃣ AI Agents in LangGraph - Free course with Harrison Chase and DeepLearning.AI.
• 5️⃣ LangGraph docs - Graph-based orchestration for long-running, stateful agents.
• 5️⃣ LlamaIndex Workflows - Event-driven workflows.
• 6️⃣ Building Effective AI Agents - The reference post on agent vs workflow design.
• 6️⃣ Stop Building Agent Demos - My article on the demo-to-production gap.
• 6️⃣ Agents and Workflows - My article on when multi-agent is overengineering.
• 6️⃣ Agent Architecture Guide - My 13-question decision framework.
• 7️⃣ 12-Factor Agents - Production-agent checklist.
• 7️⃣ Agentic AI Engineering - Our advanced agent course with two shipped agent capstones. Paid.
• 8️⃣ How we built our multi-agent research system - Anthropic's real architecture behind a shipped multi-agent product.

The model is not your system. The tool layer, state layer, eval layer, and deployment layer are where most of the real engineering happens.

Evaluations, observability, and harnesses

This is the layer most people skip and rediscover the hard way.

If you cannot tell whether your system is improving, you are not engineering yet. You are moving vibes around.

You should learn golden datasets, rule-based checks, LLM-as-a-judge, regression testing, traces, spans, prompt versioning, error analysis, offline evaluations, online monitoring, and harness design.

Best resources:

• 5️⃣ Your AI Product Needs Evals - Hamel Husain. Start here.
• 5️⃣ LLM Evals FAQ - Practical answers to common eval questions.
• 5️⃣ Automated Testing for LLMOps - DeepLearning.AI short course. Free.
• 5️⃣ Ragas - Open-source RAG evaluation library.
• 5️⃣ LangSmith - Hosted tracing and eval tooling from LangChain.
• 5️⃣ Arize Phoenix - Open-source observability for LLM applications.
• 6️⃣ Harness Engineering: The Missing Layer Behind AI Agents - My article on why harnesses separate prototypes from production.
• 6️⃣ Harness engineering - OpenAI's framing of the same layer for coding agents.
• 6️⃣ A Field Guide to Rapidly Improving AI Products - Error analysis and data flywheels in practice.
• 6️⃣ Task-Specific LLM Evals that Do & Don't Work - Eugene Yan on where LLM-as-judge helps and where it misleads.
• 7️⃣ Agent Observability and Evaluation: A 2026 Developer's Guide - Long-form practical guide.
• 8️⃣ Inspect AI - UK AI Safety Institute's open-source framework for LLM evals.

Before calling anything production-ready, add evals, tracing, logging, cost tracking, latency checks, and a way to inspect failures.

Fine-tuning, multimodal, voice, and deployment

Fine-tuning still matters, but it is not the first hammer most teams should reach for.

Only fine-tune after you understand the baseline and have evals. Otherwise you are tuning toward a blurry target.

Fine-tuning resources:

• 5️⃣ Building LLMs for Production - The fine-tuning chapters alone are useful for many teams. Paid.
• 5️⃣ Hugging Face smol fine-tuning course - Free, code-first walkthrough.
• 5️⃣ OpenAI model optimization guide - Official docs.
• 6️⃣ Hugging Face PEFT docs - LoRA and related methods.
• 7️⃣ How to Fine-Tune LLMs in 2025 with Hugging Face - Philipp Schmid's strong recent how-to.
• 8️⃣ Improving LoRA: Implementing DoRA from Scratch - Sebastian Raschka.

Multimodal and document understanding:

• 4️⃣ Anthropic Vision docs
• 4️⃣ OpenAI vision guide
• 4️⃣ Google Gemini multimodal capabilities
• 5️⃣ Docling - IBM's open-source document extraction toolkit.
• 5️⃣ Document AI: From OCR to Agentic Doc Extraction - Free DeepLearning.AI course.

Voice and realtime AI:

• 4️⃣ ElevenLabs docs - Production voice cloning and streaming text-to-speech.
• 4️⃣ Deepgram docs - Low-latency speech-to-text.
• 5️⃣ OpenAI Realtime API - Native speech-to-speech and tool use.
• 5️⃣ Gemini Live API - Google's realtime multimodal endpoint.
• 5️⃣ Pipecat - Open-source voice agent framework.
• 5️⃣ LiveKit Agents - Realtime agent infrastructure.

Deployment, inference, and open-weight models:

• 4️⃣ Ollama - Easiest way to run open models locally.
• 4️⃣ LM Studio - Graphical interface for local inference.
• 6️⃣ vLLM docs - High-throughput inference server.
• 6️⃣ SGLang - Structured generation and batching.
• 6️⃣ Text Generation Inference - Hugging Face's production serving stack.
• 6️⃣ llama.cpp - CPU and edge inference with GGUF quantization.
• 5️⃣ Modal docs - Serverless GPU compute with a clean Python interface.
• 7️⃣ BentoML LLM Inference Handbook - Free, thorough handbook on inference economics.
• 5️⃣ LiteLLM - Open-source proxy for 100+ LLM providers.
• 5️⃣ OpenRouter - Hosted router across many models.

You should be able to explain why you chose an API model or an open-weight model, why the latency and cost tradeoff makes sense, how the system behaves when a dependency fails, and how you would debug a bad output in production.

AI coding agents and developer tools

How AI engineers work changed in 2025-2026.

Coding agents and agent-native editors are now part of daily practice. Use them. But again, do not outsource your thinking.

Tools worth learning:

• 3️⃣ Claude Code - Anthropic's command-line coding agent.
• 3️⃣ Cursor - Agent-native IDE.
• 3️⃣ GitHub Copilot - Default enterprise AI coding assistant.
• 3️⃣ Codex CLI - OpenAI's long-horizon coding agent.
• 3️⃣ Gemini CLI - Google's open-source command-line agent.
• 3️⃣ Windsurf - Agent-native editor focused on context and flow.

Articles worth reading:

• 4️⃣ Here's how I use LLMs to help me write code - Simon Willison.
• 4️⃣ AI-assisted development needs automated tests - Simon Willison.
• 4️⃣ Identify, solve, verify - Simon Willison's core loop.
• 4️⃣ How to Solve It With Code - Jeremy Howard's AI-assisted problem-solving course.
• 5️⃣ What is agentic engineering? - Simon Willison's working definition.
• 6️⃣ Harness engineering: leveraging Codex in an agent-first world - OpenAI's practical framing.
• 6️⃣ Claude Code: How to Build, Evaluate, and Tune AI Agent Skills - Practical SKILL.md guide.

Rule of thumb: pick one coding agent, commit to it for a month, and learn its scaffolding well. Rotating between tools every two days is usually slower than mastering one.

AI safety, security, and guardrails

AI safety is not optional.

If your AI system can search the web, call tools, touch private data, or send actions into other software, you need to think about risk early.

Learn prompt injection, sensitive data handling, system prompt leakage, tool permissions, excessive agency, overreliance, output validation, human review thresholds, red teaming, and governance.

Start here:

• 6️⃣ OWASP Top 10 for LLM Applications 2025 - Canonical security list.
• 6️⃣ OWASP GenAI Security Project - LLM security reference.
• 7️⃣ NIST AI Risk Management Framework - US government reference framework.
• 6️⃣ OpenAI Safety Evaluations Hub - Public safety evaluation results.
• 7️⃣ The Lethal Trifecta for AI Agents - Private data, untrusted content, and external communication.
• 7️⃣ Embrace The Red - Johann Rehberger's red-teaming write-ups.
• 8️⃣ Design Patterns for Securing LLM Agents against Prompt Injections - Practical defenses.
• 6️⃣ Guardrails AI - Validators and schemas for LLM output.
• 6️⃣ Red Teaming LLM Applications - Free DeepLearning.AI course.
• 7️⃣ NVIDIA NeMo Guardrails
• 7️⃣ PyRIT - Microsoft's red teaming orchestration tool.

Treat LLM output like work from a fast intern with occasional alien instincts. You do not blindly trust it. You design systems around it.

More resources

Most of the time, the best way to learn is to learn with other people.

Join communities, ask questions, share projects, read what practitioners are learning in public, and stay close to the builders who are shipping real systems.

Communities:

• 2️⃣ Towards AI Discord - 80,000+ builders, events, and channels for RAG, agents, fine-tuning, and jobs.
• 2️⃣ Learn AI Together - My nearly 100,000-member server for AI enthusiasts, study groups, and learning teammates.
• 3️⃣ Hugging Face Discord
• 3️⃣ LangChain Discord
• 3️⃣ LlamaIndex Discord
• 4️⃣ MLOps Community
• 4️⃣ r/LocalLLaMA
• 3️⃣ r/Rag
• 3️⃣ r/AI_Agents
• 5️⃣ r/MachineLearning

Cheat sheets and decision guides:

• 4️⃣ AI Engineering Cheatsheets
• 4️⃣ AI Engineering Playbook
• 4️⃣ Agent Architecture Guide
• 4️⃣ Anti-Slop AI Writing Guide
• 4️⃣ Towards AI Free Resource Library

Newsletters:

• 3️⃣ Towards AI Newsletter - Weekly AI news, benchmarks, pricing, and opinion.
• 3️⃣ Last Week in AI
• 3️⃣ The Batch
• 4️⃣ Louis-François Bouchard's Substack
• 4️⃣ Latent Space
• 5️⃣ Decoding AI - Paul Iusztin on production ML and AI engineering.
• 6️⃣ Interconnects - Nathan Lambert on post-training, RLHF, and reasoning.
• 6️⃣ Ahead of AI - Sebastian Raschka.
• 5️⃣ The Neural Maze  -  Miguel Otero Pedrido

Podcasts and blogs:

• 4️⃣ The What's AI Podcast
• 4️⃣ Latent Space Podcast
• 5️⃣ Machine Learning Street Talk
• 4️⃣ Simon Willison
• 4️⃣ Hamel Husain
• 4️⃣ ThursdAI - Alex Volkov's weekly live show
• 4️⃣ Eugene Yan
• 4️⃣ Chip Huyen
• 6️⃣ Lilian Weng
• 6️⃣ Shreya Shankar
• 6️⃣ Jason Liu
• 6️⃣ Philipp Schmid

People to follow:

• 4️⃣ Louis-François Bouchard
• 4️⃣ Andrew Ng
• 4️⃣ swyx
• 4️⃣ Harrison Chase
• 4️⃣ Alex Volkov 
• 4️⃣ Logan Kilpatrick
• 4️⃣ Omar Sanseviero
• 5️⃣ Simon Willison
• 5️⃣ Hamel Husain
• 5️⃣ Jason Liu
• 5️⃣ Chip Huyen
• 5️⃣ Philipp Schmid
• 6️⃣ Andrej Karpathy
• 6️⃣ Sebastian Raschka
• 6️⃣ Shreya Shankar
• 6️⃣ Nathan Lambert
• 6️⃣ Lilian Weng
• 8️⃣ Yann LeCun

The complete GitHub repo has many more resources, and I will keep updating it throughout 2026 as the stack changes.

How to find an AI engineering job

The market is messy, but the signal is clearer than people think.

Companies want people who can take a vague problem, make reasonable assumptions, build a baseline, evaluate it, document tradeoffs, and ship something testable.

That is closer to real work than trivia-style interviews.

Best resources:

• 4️⃣ AI Engineering Cheatsheets - Decision tables you can reference in interviews.
• 4️⃣ Towards AI Academy - Certificate programs and portfolio projects built for hiring.
• 5️⃣ What I Look For When Hiring AI Engineers - My lessons from hiring and interviewing AI engineers.
• 5️⃣ How to Work and Compound with AI - Eugene Yan on how serious engineers work with coding agents and compound over time.
• 5️⃣ Identify, solve, verify - Simon Willison on the core skill employers are looking for.
• 5️⃣ Your job is to deliver code you have proven to work - Simon Willison on the shift in programming work.
• 7️⃣ How to Land a Frontier Lab Job - Vlad Feinberg on building rare skills at the edges of the LLM stack.

What to do concretely:

Ship two to four public projects that are small but serious.

Write short READMEs that explain your architecture choices, cost and latency tradeoffs, and failure modes. Include tests and at least one evaluation dataset. Show traces, monitoring, or experiment logs when relevant.

Learn to explain why you chose not to use an agent in some places. Be able to compare prompting, RAG, fine-tuning, workflows, and agents for a given problem.

Many candidates can now generate code. Far fewer can show judgment.

Conclusion

This was a guide for anyone with zero or a small background in programming, AI, or machine learning who wants to become an AI engineer in 2026.

It is a non-exhaustive list. You can use more resources, fewer resources, or a completely different order depending on your background and learning style.

The important part is not to finish the list as fast as possible.

The important part is to build judgment.

Learn enough theory to avoid magical thinking. Learn enough tooling to build quickly. Learn enough evaluation to trust what you ship. Learn enough product judgment to avoid building the wrong thing faster.

And above all, keep shipping.

That is still the shortcut.

Here is the GitHub repository with the full, updated list:

GitHub - louisfb01/start-ai-engineering

If this guide helps you, please star the repo and share it with one person who wants to get into AI engineering this year. That is how it keeps reaching the right people.

Tag me on X @Whats_AI or LinkedIn if you share it.

If you would like to support this work, the best way is to join one of our Towards AI Academy courses or subscribe to What's AI on YouTube.

Thank you so much for reading, and I wish you all the best in your future AI engineering career.

• Signing out.