How LLM Inference Works: From Prompt to Generated Tokens
Understand how LLM inference works, including tokenization, prefill and decode phases, KV cache, attention, sampling, and how transformer models generate text token by token in production systems.
LLM Architecture Overview: How Modern Language Models Are Built
A system-level overview of LLM architecture, including transformer blocks, encoder vs decoder models, attention mechanisms, training and inference flow, and how all components fit together in modern AI systems.
LLM Attention Mechanism: How Models Focus on What Matters
Understand the attention mechanism that powers Large Language Models (LLMs), including self-attention, query-key-value vectors, attention scores, multi-head attention, and why attention replaced recurrent neural networks.
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.