Topic
Gpt
Learn Gpt as a connected topic across chapters, concepts, simulations, and interview reasoning.
10 Concepts12 Articles3h 32m
Overview
Learn Gpt as a connected topic across chapters, concepts, simulations, and interview reasoning.
How this topic helps
Llm
Ai
Generative Ai
Architecture
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Articles
12 matched articles
Article 1How GPT (LLM) Works: The Next Word PredictorTLDR: At its core, GPT asks one question, repeated: "Given everything so far, what is the most likely next token?" Tokens are not words — they're subword units. The Transformer architecture uses self-15 minArticle 2Build vs Buy: Deploying Your Own LLM vs Using ChatGPT, Gemini, and Claude APIsTLDR: Use the API until you hit $10K/month or a hard data privacy requirement. Then add a semantic cache. Then evaluate hybrid routing. Self-hosting full model serving is only cost-effective at > 50M 31 minArticle 3LLM Model Selection Guide: GPT-4o vs Claude vs Llama vs Mistral — When to Use WhichTLDR: 🧠 Choosing the right LLM can save you 80% on costs while maintaining quality. This guide provides a decision framework, cost comparison, and practical examples to help engineering teams select 23 minArticle 4GPTQ vs AWQ vs NF4: Choosing the Right LLM Quantization PipelineTLDR: GPTQ, AWQ, and NF4 all shrink LLMs, but they optimize different constraints. GPTQ focuses on post-training reconstruction error, AWQ protects salient weights for better quality at low bits, and 15 min
Article 5Sparse Mixture of Experts: How MoE LLMs Do More With Less ComputeTLDR: Mixture of Experts (MoE) replaces the single dense Feed-Forward Network (FFN) layer in each Transformer block with N independent expert FFNs plus a learned router. Only the top-K experts activat27 minArticle 6RLHF Explained: How We Teach AI to Be NiceTLDR: A raw LLM is a super-smart parrot that read the entire internet — including its worst parts. RLHF (Reinforcement Learning from Human Feedback) is the training pipeline that transforms it from a 14 minPage 1 of 2