LLM Fundamentals

The core mechanics of how LLMs work and the practical parameters engineers actually tune: attention, tokenization, sampling, structured output, and cost/latency trade-offs.

Read

45 min

Practice MCQs

25

Interview QA

25

Edition

v4

Editorial status

Reviewed

Relevant for

AI Engineer,Agentic AI Engineer,Forward Deployed Engineer (FDE)

Scope: Transformer, BPE, SentencePiece, GPT-3, scaling laws, Chinchilla, nucleus sampling, RoPE, FlashAttention, GQA, PagedAttention, speculative decoding, GPTQ, Model Cards, and NIST GenAI references reviewed 2026-09-06.