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Ai Agents

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

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

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Llm
Python
Langchain
Langgraph

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Article 1Multistep 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 2AI 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 minArticle 3RAG vs Fine-Tuning: When to Use Each (and When to Combine Them)šŸ“Œ TL;DR Summary Use RAG when facts change frequently and answers must be source-grounded. Use fine-tuning when you need stable behavior: tone, format, and domain-specific reasoning. Use RAG + fine-t31 minArticle 4Build 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 5LangChain Tools and Agents: The Classic Agent LoopšŸŽÆ Quick TLDR: The Classic Agent Loop TLDR: LangChain's @tool decorator plus AgentExecutor give you a working tool-calling agent in about 30 lines of Python. The ReAct loop — Thought → Action → Obser21 minArticle 6LangChain 101: Chains, Prompts, and LLM IntegrationTLDR: LangChain's LCEL pipe operator (|) wires prompts, models, and output parsers into composable chains — swap OpenAI for Anthropic or Ollama by changing one line without touching the rest of your c19 min

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