The Must-Know Topics for an LLM Engineer
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A structured overview of the full LLM engineering stack, covering tokenization (BPE, embeddings, positional encodings), transformer architectures (encoder-only, decoder-only, encoder-decoder), training stages (pre-training, supervised fine-tuning with LoRA, RLHF with PPO/DPO/GRPO), hallucination mitigation via RAG, inference optimization techniques (KV-caching, FlashAttention, quantization, speculative decoding, MoE), prompt engineering best practices, and evaluation strategies ranging from BLEU/ROUGE to LLM-as-a-judge and production drift monitoring.
Table of contents
Converting letters to numbersModel ArchitectureHallucination in LLMsOptimizationPrompt engineeringEvaluationLLM CriticismSummaryLiked the author? Stay connected!102 Impressions