मुफ़्तArtificial Intelligence
Generative AI Engineer - Senior Prep
For engineers who want to build an LLM, not just call one: write a GPT from scratch in code with Andrej Karpathy, pretrain your own GPT-2, then fine-tune (LoRA/RLHF) and ship a small LLM app end-to-end. Capped with senior-level system-design interview prep.
Build the end-to-end mental model senior interviewers probe for: pretraining, post-training, tokenization, sampling, and why models hallucinate. Skip the hype and understand the full training stack.
- The full LLM training stack: pretraining vs post-training
- How internet-scale datasets (Common Crawl, FineWeb) are built and filtered
- Base models vs assistant models and what fine-tuning really changes
- Why LLMs hallucinate, and how RLHF shapes behavior
- Reasoning models, tool use, and where the field is heading
- Talking-points you can reuse verbatim in senior interviews
- What the two files that make up an LLM really are
- Scaling laws and the compute/data/parameter trade-off
- The LLM 'operating system' framing for tools and agents
- Prompt injection, jailbreaks, and other security failure modes
- A crisp 1-hour narrative you can whiteboard from memory