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These 12 definitions are a great starting point.

Understand them, and you'll ask better questions, spot bad assumptions faster, and build systems that scale.

𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 (𝗟𝗟𝗠)
An AI trained on massive datasets to understand and generate human-like text.

𝟮. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚)
Connects an AI to your private, real-time data sources like PDFs, databases, and documents.

𝟯. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀
Systems designed to use tools like email, calendars, and code to complete multi-step goals.

𝟰. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗼𝘄
The total amount of information an AI can keep in mind during a single chat.

𝟱. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴
The process of training a pre-existing model on a small, niche dataset.

𝟲. 𝗧𝗼𝗸𝗲𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻
How AI breaks text into smaller chunks to process them mathematically.

𝟳. 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗮𝗹𝗶𝘁𝘆
The ability for a single AI to process text, images, audio, and video simultaneously.

𝟴. 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻
When an AI generates factually incorrect information with high confidence.

𝟵. 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀
Converting words or images into numbers so the AI can measure how related they are.

𝟭𝟬. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀
Technical boundaries and filters that prevent an AI from going off-topic or being biased.

𝟭𝟭. 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀
The internal connections in a model that determine its complexity and intelligence.

𝟭𝟮. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗛𝘂𝗺𝗮𝗻 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 (𝗥𝗟𝗛𝗙)
Training an AI using human feedback to align its behaviour with our values.

Understanding data doesn’t have to be complicated.

Take one concept at a time and go deeper.

Depending on your role, you don't need to be an expert.

You just need to know enough to create value.

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