LLM fundamentals — Understand the thing everything else sits on
If you only ever do one item on this page, do the first one. Almost every confused conversation I have about AI systems traces back to someone never having watched this.
Track 1 of the AI engineering roadmap by K Laxman — about 6 hours across 1 stage.
The whole pipeline, end to end
You can trace a token from raw internet text all the way to a chat reply, naming every stage.
pretraining · tokenisation · transformers · inference · SFT · RLHF · hallucination · tool use · reasoning models
- Deep Dive into LLMs like ChatGPT — Andrej Karpathy (video, free, ~3.5h, rated 10/10). Do this first. Nothing else on the internet takes you from raw pretraining data to RLHF and reasoning models in one honest, unhurried pass — including why hallucination happens rather than just that it does.
- Attention Is All You Need — Vaswani et al., 2017 (paper, free, ~1.5h, rated 9/10). Read it AFTER the video, not before. It is eight pages and it will feel obvious, which is exactly the point at which you have understood it.
Build this — Re-implement decoding. Take any provider that returns logprobs and implement greedy, temperature and top-p sampling yourself. Write a README explaining, in your own words, when you'd use each. It is thirty lines of code and it makes §0.5 permanent.
You're done when: You can explain, without notes, why a model hallucinates confidently rather than saying 'I don't know'.
Put to work in: Jarvis — six interchangeable providers behind one loop · CAMSCode — enterprise AI coding assistant
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