Archives
All the articles I've archived.
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Why Embeddings Matter
Updated:A deep dive into what embeddings are, why they matter, and how they power modern AI, semantic search, and RAG-based systems.
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What LLMs Do at Inference: A Deep Dive Under the Hood
Updated:A step-by-step, reference-backed explanation of what happens during LLM inference: tokenization, embeddings, prefill & decode phases, KV caching, decoding strategies, bottlenecks and optimizations like quantization, FlashAttention and speculative decoding.
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Transformers in AI
Updated:The Architecture That Revolutionized Machine Learning
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Top-k vs. Nucleus Sampling
Updated:Decoding the Secrets of AI Text Generation
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GPU vs TPU
Updated:Decoding the Battle of AI Accelerators in 2025
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Why Does Retrieval-Augmented Generation (RAG) Exist?
Updated:In the rapidly evolving world of artificial intelligence, large language models (LLMs) like GPT-4 or Grok have transformed how we interact with technology.
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Understanding Tokenizers in AI — A Deep Dive into ChatGPT, Grok, and Gemini
Updated:A complete guide to tokenizers in modern LLMs, covering BPE, WordPiece, SentencePiece, Unigram, and how ChatGPT, Grok, and Gemini tokenize text. Includes examples, real-world impact, and why tokenization is the foundation of AI.
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KV Cache Explained
Updated:A Deep Dive into Transformer Optimization