START HERE · PATH 01
Become a GenAI engineer
For students, developers and data scientists. You need basic Python. Seven stages, in the order you need them, every video and guide free. Skip any stage you already know.
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STAGE 01
LLM foundations
Know what the model is actually doing.
- after this
- You can explain tokenization, context windows, attention, the KV cache, LoRA and the rest of the core vocabulary in one line each.
- skip if
- You can already explain how attention and the KV cache work.
- VIDEO · 13 MINGenerative AI explained from scratch: model, system, application
- PDF GUIDE14 LLM concepts every AI engineer should know
- VIDEO · GO DEEPERHow large reasoning models work: the architecture explained
- VIDEO · GO DEEPERLLMs waste 70% of their compute: how VL-JEPA fixes it
- PDF · GO DEEPER15 AI research papers every AI engineer should read
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STAGE 02
Pick the architecture
Decide what you're building before you build it.
- after this
- Given a problem, you can say whether it needs a prompt, retrieval, fine-tuning or an agent, and why.
- skip if
- You already choose between RAG and agents with confidence.
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STAGE 03
Build a RAG pipeline
Get one working end to end.
- after this
- You can build a working RAG app and name every step in the pipeline.
- skip if
- You've already shipped a RAG app.
- SHORT · 1:30How RAG works: the full pipeline in 8 steps
- VIDEO · 8 MINLangChain course, part 1: from RAG to agents in 8 minutes
- VIDEO · 13 MINLangChain course, part 2: a complete setup that actually works
- VIDEO · 11 MINHow to build an AI app with LangChain: RAG and agents explained
- VIDEO · 8 MINLangChain vs LangGraph vs LangSmith: which one should you use?
- VIDEO · GO DEEPERLangChain course, part 3: working with different LLMs, with code
- VIDEO · GO DEEPERLangChain course, part 4: better prompts with messages
- VIDEO · GO DEEPERLangChain course, part 7: build a multimodal AI app
- PDF · GO DEEPER21 RAG research papers, from DPR to Agentic RAG
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STAGE 04
Make retrieval good
Most wrong answers start with the wrong chunks.
- after this
- You can pick an embedding model, choose between keyword, vector and hybrid search, and tune top-k on purpose.
- skip if
- You know why HNSW is approximate and when BM25 beats embeddings.
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STAGE 05
Evaluate and fix
Measure it, find what's broken, then fix the right layer.
- after this
- You can score a RAG system with the right metrics, tell a retrieval failure from a generation failure, and know when fine-tuning is the fix and when it isn't.
- skip if
- You already run Ragas-style evaluations on your pipelines.
- PDF GUIDE9 RAG evaluation metrics, and how to measure each one
- SHORT · 0:59RAG found the right doc. So why is the answer wrong?
- VIDEO · 9 MINHow to build AI systems that reduce hallucinations (it's not the LLM)
- VIDEO · 9 MINWhy RAG fails in production, and can CLARA fix it?
- SHORT · 0:25Stop fine-tuning to fix wrong answers
- PDF GUIDE15 LLM fine-tuning techniques, with the paper behind each one
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STAGE 06
Agents and tools
When answering isn't enough and the system has to act.
- after this
- You can build a tool-using agent, connect tools through MCP, and choose a multi-agent pattern.
- skip if
- You've built agents that call tools in production.
- SHORT · 0:53How AI agents work: the pipeline in 6 steps
- VIDEO · 11 MINBefore you build an AI agent, watch this
- VIDEO · 20 MINHow AI agents decide what to do next
- VIDEO · 13 MINLangChain course, part 5: tool calling, so agents take real actions
- VIDEO · 16 MINHow to build an AI agent with LangChain
- VIDEO · 6 MINModel Context Protocol: the missing layer between AI and tools
- VIDEO · 10 MINWhy RAG isn't enough anymore (MCP explained)
- SHORT · 0:49Where MCP actually runs
- VIDEO · 21 MINMulti-agent systems: 4 orchestration patterns
- VIDEO · GO DEEPERAI agents vs agentic AI: the difference most people get wrong
- VIDEO · GO DEEPER6 AI agent architectures every developer should know
- VIDEO · GO DEEPER5 AI agent architectures: multi-agent design patterns explained
- VIDEO · GO DEEPERAI agents in 2026: everything you need to know
- VIDEO · GO DEEPERHarness engineering: the architecture behind reliable AI agents
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STAGE 07
Prove it
Turn what you learned into something you can show.
- after this
- You have a GenAI project you can show, and explain, in an interview.
- VIDEO · 36 MIN6 Gen AI projects that prove you're ready for an AI job
- VIDEO · 10 MINYour data science portfolio is losing you jobs: 7 mistakes to fix
- PROJECTAccuracy vs cost: a case study with the code and data on GitHub
- THE SPRINTBuild a production RAG system in 4 weeks, with a group
- VIDEO · GO DEEPERZero to AI engineer in 6 months: the exact roadmap
- VIDEO · GO DEEPER5 high-paying AI roles, with a complete career roadmap
- VIDEO · GO DEEPERBuild these 5 data science projects to land your first job
FINISHED THE PATH?
Build one with others, with deadlines.
The Sprint is 4 weeks, one production RAG system, weekly reviews and a demo for your portfolio.
CHANGELOG · THIS PATH IS KEPT UP TO DATE
- 29 OCT 2026Added the 10 embedding techniques guide to stage 04.
- 29 OCT 2026First version: 7 stages from 38 videos and 6 PDF guides. Items marked "go deeper" are optional.