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Generative Ai

Learn Generative Ai as a connected topic across chapters, concepts, simulations, and interview reasoning.

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Learn Generative Ai as a connected topic across chapters, concepts, simulations, and interview reasoning.

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Ai
Llm
Deep Learning
Ai Agents

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Article 1Mastering Prompt Templates: System, User, and Assistant Roles with LangChainTLDR: A production prompt is not a string — it is a structured message list with system, user, and optional assistant roles. LangChain's ChatPromptTemplate turns this structure into a reusable, testab14 minArticle 2Prompt Engineering Guide: From Zero-Shot to Chain-of-ThoughtTLDR: Prompt Engineering is the art of writing instructions that guide an LLM toward the answer you want. Zero-Shot, Few-Shot, and Chain-of-Thought are systematic techniques — not guesswork — that can13 minArticle 3Multistep AI Agents: The Power of PlanningTLDR: A simple ReAct agent reacts one tool call at a time. A multistep agent plans a complete task decomposition upfront, then executes each step sequentially — handling complex goals that require 5-115 minArticle 4How to Develop Apps Using LangChain and LLMsTLDR: LangChain is a framework that simplifies building LLM applications. It provides abstractions for Chains (linking steps), Memory (remembering chat history), and Agents (using tools). It turns raw17 minArticle 5How 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 6Diffusion Models: How AI Creates Art from NoiseTLDR: Diffusion models work by first learning to add noise to an image, then learning to undo that noise. At inference time you start from pure static and iteratively denoise into a meaningful image. 12 min

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