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AiMental ModelTradeoffsFailure ModesInterview ReasoningChain of Thought Prompting
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1Chain of Thought Prompting: Teaching LLMs to Think Step by StepTLDR: Chain of Thought (CoT) prompting tells a language model to reason out loud before answering. By generating intermediate steps, the model steers itself toward correct conclusions — turning guessw27 min2How AI Coding Agents Work: Models, Context, Sessions, and MemoryTLDR: An AI coding agent is an LLM stapled to a tool registry, wrapped in an orchestration loop that painstakingly rebuilds state on every single API call — because the model itself is completely stat34 min3Skills vs LangChain, LangGraph, MCP, and Tools: A Practical Architecture GuideTLDR: These are not competing ideas. They are layers. Tools do one action. MCP standardizes access to actions and resources. LangChain and LangGraph orchestrate calls. Skills package business outcomes15 min4RLHF 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 min5Mastering 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 min6Prompt 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 min7Multistep 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 min8LoRA Explained: How to Fine-Tune LLMs on a BudgetTLDR: Fine-tuning a 7B-parameter LLM updates billions of weights and requires expensive GPUs. LoRA (Low-Rank Adaptation) freezes the original weights and trains only tiny adapter matrices that are add13 min9How 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 min10Guide to Using RAG with LangChain and ChromaDB/FAISSTLDR: RAG (Retrieval-Augmented Generation) gives an LLM access to your private documents at query time. You chunk and embed documents into a vector store (ChromaDB or FAISS), retrieve the relevant chu14 min11Diffusion 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 min12AI Agents Explained: When LLMs Start Using ToolsTLDR: A standard LLM is a brain in a jar — it can reason but cannot act. An AI Agent connects that brain to tools (web search, code execution, APIs). Instead of just answering a question, an agent exe13 min
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