6 docs tagged with "AI Foundations"

LLM Components Explained: Understanding the Building Blocks of Large Language Models

Learn the key components of modern LLMs including tokenization, embeddings, transformers, attention, training, inference, and model parameters.

LLM Context Window Explained: How AI Remembers and Processes Information

Learn what the context window is in LLMs, how token limits work, why context length matters, and how it impacts memory, reasoning, and real-world AI applications like RAG and chat systems.

LLM Embeddings Explained: How AI Understands Meaning and Similarity

Learn what embeddings are, how they convert text into vectors, why they are essential for semantic search and RAG systems, and how modern LLMs represent meaning in high-dimensional space.

LLM Model Parameters Explained: What 7B, 70B, and 405B Really Mean

Learn what LLM model parameters are, how they are trained, and why 7B, 70B, and 405B models differ in capability, memory, and cost.

LLM Tokens Explained: The Building Blocks of Large Language Models

Learn what tokens are in Large Language Models, how tokenization works, why token counts matter for context windows and pricing, and how modern LLMs process text.

LLM Training Explained: How Large Language Models Learn

Learn how LLMs are trained through pretraining, fine-tuning, RLHF, DPO, and alignment. Understand how data becomes model intelligence.