Tag: large language models

Predicting Performance Gains from Scaling Large Language Models

by Phillip Ramos

Scaling laws let you predict exactly how much performance improves when you increase model size, data, or compute. Learn how math, not just bigger models, drives AI breakthroughs-and why efficiency now beats raw scale.

Localization and Translation Using Large Language Models: How Context-Aware Outputs Are Changing the Game

by Phillip Ramos

Large language models are transforming localization by understanding context, tone, and culture - not just words. Learn how they outperform traditional translation tools and what it takes to use them safely and effectively.

Why Tokenization Still Matters in the Age of Large Language Models

by Phillip Ramos

Despite the rise of massive language models, tokenization remains essential for accuracy, efficiency, and cost control. Learn why subword methods like BPE and SentencePiece still shape how LLMs understand language.

Key Components of Large Language Models: Embeddings, Attention, and Feedforward Networks Explained

by Phillip Ramos

Learn how embeddings, attention, and feedforward networks form the core of modern large language models like GPT and Llama. No jargon, just clear explanations of how AI understands and generates human language.