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Deep research agents have gotten significantly better. Tools like Perplexity, OpenAI Deep Research, and Gemini can search, synthesize, and return structured answers across dozens of sources. The quality of the output has improved fast. What these tools don’t make visible is the process. When they stop searching, you don’t know if the work is done or the budget ran out. When two sources conflict, you don’t know if that got noticed or smoothed over. The output looks the same either way. ...

This document is my personal deep-dive into how modern open-source GPT-style language models are actually built. It’s not meant to be a full survey of the literature; instead, it captures the pieces that helped me build an intuition for these models: how text becomes vectors, how positional information is injected, how attention is optimized, and how FFN variants like MoE increase capacity without blowing up compute. 1. Tokenization & Embeddings The journey from human-readable text to a format the model understands begins here. ...

This guide explores the core attention variants in modern transformers, focusing on the mechanisms themselves: Multi-Head Attention (MHA), Multi-Query Attention (MQA), and Grouped-Query Attention (GQA). We’ll understand why each exists and their fundamental architectural differences. Quick Overview Self-Attention: Each token looks at other tokens to build contextualized representations Multi-Head Attention (MHA): Multiple independent attention “heads” in parallel; each head has its own Q, K, V projections Multi-Query Attention (MQA): Share Key/Value projections across all query heads; reduces parameters significantly Grouped-Query Attention (GQA): Groups of query heads share K/V projections; balances expressiveness and efficiency 1. Self-Attention Foundations Core Intuition Self-attention is a content-based lookup over the sequence: ...

Introduction Language models are mathematical functions; they operate on numbers, not raw text. Tokenization is the crucial first step in converting human-readable text into a sequence of integers (tokens) that a model can process. These tokens are then mapped to embedding vectors. 1. Naive Approaches and Their Flaws Word-Level Tokenization The most intuitive approach: split text by spaces and punctuation. Problems: Vocabulary Explosion: A language like English has hundreds of thousands of words. The model’s vocabulary would be enormous, making the final embedding and output layers computationally massive. Out-of-Vocabulary (OOV) Words: If the model encounters a word not seen during training (e.g., a new slang term, a typo, or a technical name), it has no token for it. It typically maps it to an <UNK> (unknown) token, losing all semantic meaning. Poor Generalization: The model treats eat, eating, and eaten as three completely separate, unrelated tokens. It fails to capture the shared root eat, making it harder to learn morphological relationships. Character-Level Tokenization The opposite extreme: split text into individual characters. ...

Introduction Reinforcement Learning (RL) has exploded in popularity — first in game-playing agents, and now in large language models via methods like RLHF (Reinforcement Learning from Human Feedback). These approaches don’t just help models learn context better; they also improve reasoning by teaching them to “think in steps.” My fascination with RL began when the GPT-3 paper was published and ChatGPT emerged as the so-called “tool of the decade.” I wanted to go beyond using these models — I wanted to understand how they work under the hood. That meant building RL concepts from the ground up: deriving equations, implementing toy solutions in environments like CartPole and FrozenLake, and seeing theory come alive in code. ...