The complete roadmap for becoming dangerous at LLM engineering from zero to production : Watching tutorials won’t make you an LLM engineer. Building these projects will: - Train a small working model - Master embeddings from scratch - Create your own tokenizer - Code RoPE and ALiBi yourself - Write attention by hand - Construct multi-head attention - Assemble a complete Transformer block - Test different training objectives - Implement generation strategies - Add speculative decoding - Build efficient KV caching - Explore MQA, GQA, and MLA - Solve long-context challenges - Understand FlashAttention deeply - Calculate real hardware limits - Build a simple Mixture-of-Experts - Study sparse vs dense trade-offs - Experiment with state-space models - Try diffusion-based language models - Design solid data pipelines - Generate and use synthetic data - Verify scaling laws yourself - Go through SFT to DPO to RLHF to GRPO - Apply proper quantization - Set up serving infrastructure - Build reliable evaluation systems - Create RAG pipelines - Add tool use and agent loops - Connect vision adapters - Practice interpretability techniques - Run serious red-teaming - Ship one complete end-to-end system Finish even half of these and you’ll be ahead of 95% of people talking about LLMs.