🔤 Agent: An AI that doesn't just answer questions, it takes actions. Books your flight. Sends your email. Browses the web. Most current 'AI agents' are still clunky, but the category is exploding.

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Myth: AI needs perfect data to work.

Reality: A nonprofit just fed Claude a decade of messy spreadsheets. Organized in 20 minutes. Most of your data is messy anyway. AI handles real-world chaos better than you'd think. #AIMyths #BeginnerAI

🔤 RAG: Retrieval-Augmented Generation. When AI looks up information from a specific source (like your company docs) before answering. Reduces hallucinations and lets AI know about things it wasn't trained on.

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🔤 LLM: Large Language Model. The type of AI behind ChatGPT, Claude, and Gemini. It learned to predict the next word by reading huge amounts of text. Turns out predicting words really well = looking like intelligence.

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🔤 Fine-tuning: Taking a general AI model and training it more on a specific topic so it gets better at that one thing. Like sending a generalist doctor to medical school for a specialty.

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🔤 Context Window: How much an AI can 'remember' in one conversation. Measured in tokens. A 200K context window holds about 150,000 words. Once you exceed it, the AI starts forgetting earlier parts.

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🔤 Hallucination: When an AI confidently makes up something false. The AI isn't lying, it just generated text that sounds right based on patterns in its training data. Always double-check critical info.

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🔤 Token: A chunk of text an AI reads or writes. Roughly 4 characters or 3/4 of a word. When you hear 'this model has a 200K context window,' it means it can hold 200K tokens (about 150K words) at once.

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🔤 Neural Network: A system of math loosely inspired by the human brain. Data goes in, passes through layers of 'neurons' that each make tiny decisions, and an answer comes out. It's behind almost every AI tool you use today.

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