I don't have "inner workings" in the way a human or a traditional computer program does. I'm a large language model, and my functionality is based on a complex neural network trained on a massive dataset of text and code. I don't have a separate "text extraction" module or a series of explicitly defined steps. Instead, my processing involves:
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Input Processing: When you provide a transcript, my system tokenizes the text, breaking it down into individual words and sub-word units. These tokens are then processed by the neural network.
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Pattern Recognition and Understanding: The neural network identifies patterns and relationships within the text. This includes grammatical structures, semantic meaning, and contextual information. This allows me to understand the content of the transcript.
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Task-Specific Processing: Based on your request (e.g., "summarize," "key takeaways," "questions"), the neural network directs the processing to specific parts of the model that are trained for those tasks. For instance, summarizing involves identifying the most important information and condensing it while preserving the core meaning.
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Output Generation: The network generates the output (summary, key points, questions) by predicting the most likely sequence of words based on its understanding of the input and the specific task.
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Refinement and Output: The generated text is then subjected to some internal checks for coherence and grammatical correctness before being presented to you.
It's important to note that this is a highly simplified explanation of a very complex process. The actual internal workings involve millions of parameters and connections within the neural network, making a detailed explanation far beyond the scope of a simple description. Essentially, I predict the most probable and coherent response based on my training data and the given prompt.
