A practical engineering guide to four core LLM prompting techniques—system prompts, few-shot examples, chain-of-thought reasoning, and structured output—covering real failure modes and production-ready patterns.

A practical walkthrough of how the Transformer architecture evolved from encoder-decoder to decoder-only models, why attention’s quadratic scaling became a hard wall, and how Mamba’s state space machines are being absorbed into hybrid architectures that dominate production today.

A practical deep-dive into how the original Transformer model shattered translation benchmarks, slashed training costs, and laid the architectural foundation for every major LLM that followed.

A practical walkthrough of why RNNs hit a fundamental wall with sequential processing and long-range dependencies, and how the Query-Key-Value attention mechanism solves both problems in one elegant step.