Technically exists to help you get better at your job by becoming more technically literate.
Understand AI or let it take your job, the choice is yours.
A context window is how much data an AI model can hold in memory at once.
A token is the basic unit of a Large Language Model's vocabulary.
Fine tuning is the process of taking a pre-trained AI model and specializing it for your specific use case.
RLHF is the final training step that turns a knowledgeable but rambling AI into the helpful assistant you know and love.
Training datasets are the examples you show an AI model so it can learn to recognize patterns and make predictions.
AI hallucination is when AI models confidently generate information that's completely made up or wrong.
Inference is a fancy term that just means using an ML model that has already been trained.
Prompt engineering is the art of talking to AI models in a way that gets you the results you actually want.
Retrieval Augmented Generation (RAG) is a way to make LLMs like GPT-4 more accurate and personalized to your specific data
AI reasoning is how artificial intelligence systems solve problems, think through complex situations, and draw conclusions from available information.
Neural networks are the mathematical brains behind modern AI—think of them as simplified versions of how your actual brain processes information.
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