Blog Post··4 min read
Why Dropout Disappeared from Large Language Models
BERT used dropout everywhere. LLaMA uses none. The reason isn't that regularization stopped mattering — it's that at trillion-token scale, data diversity IS the regularizer.
Tag: llm
Blog Post··5 min read
Prefill and Decode: The Two Phases of LLM Inference
LLM inference has two fundamentally different compute phases. Prefill processes the prompt in parallel and is compute-bound. Decode generates tokens one at a time and is memory-bandwidth-bound. Understanding both determines how you optimize.
Blog Post··5 min read
The Lifecycle of a KV Cache: From Prefill to Last Token
A request-level walkthrough of how the KV cache is populated, grown, and read during LLM inference — covering prefill, decode, memory layout, and why decode is memory-bandwidth-bound.
Blog Post··9 min read
Comparing Large Model Architectures: Attention, Normalization, and Scale
GPT-4, Gemini, LLaMA, Mistral, DeepSeek, Qwen — they all build on the same transformer skeleton. But the architectural choices diverge sharply. Here's a systematic comparison across model families.
Blog Post··8 min read
Evaluation Metrics: Precision, Recall, Calibration, and Confidence
How do you measure whether a model is actually good? The answer is a set of metrics — precision, recall, F1, perplexity, calibration, confidence intervals — each measuring something different and failing in a different way.
Blog Post··5 min read
Red-Teaming vs Automated Evals: Tradeoffs and When to Use Each
Human red-teaming finds attacks automated evals miss. Automated evals achieve scale humans can't. Here's how to combine them, and what each can and can't tell you.
Blog Post··5 min read
Runtime Guardrails: Architecture Patterns for Production AI Safety
Training-time alignment is not enough. Production AI systems need runtime layers that detect, intercept, and respond to harmful inputs and outputs. Here's how to build them.
Blog Post··8 min read
How Decoder-Only Transformers Evolved Since GPT-2
GPT-2 established the decoder-only transformer as the dominant paradigm. What followed was six years of systematic improvements — in scale, efficiency, alignment, and reasoning. Here's the arc.