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=LLM Prompt/Response Code-Event Chain= ==I. User Input Preprocessing== ===A. Tokenization and Normalization=== * The user's query or instruction is ''''inputted'''' into the system via a '''graphical user interface (GUI)''', '''command-line interface (CLI)''', or '''voice user interface (VUI)'''. * The input is then tokenized and..."

2024-08-11T12:37:35Z

Created page with "<div style="background-color:azure; border:1px outset azure; padding:0 20px; max-width:860px; margin:0 auto; "> =LLM Prompt/Response Code-Event Chain= ==I. User Input Preprocessing== ===A. Tokenization and Normalization=== * The user's query or instruction is ''''inputted'''' into the system via a '''graphical user interface (GUI)''', '''command-line interface (CLI)''', or '''voice user interface (VUI)'''. * The input is then tokenized and..."

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<div style="background-color:azure; border:1px outset azure; padding:0 20px; max-width:860px; margin:0 auto; ">
=LLM Prompt/Response Code-Event Chain=
==I. User Input Preprocessing==
===A. Tokenization and Normalization===
* The user's query or instruction is ''''inputted'''' into the system via a '''graphical user interface (GUI)''', '''command-line interface (CLI)''', or '''voice user interface (VUI)'''.
* The input is then tokenized and normalized, breaking it down into individual tokens, which are typically in the form of subwords.
* '''Text preprocessing''' techniques, such as stemming or lemmatization, may be applied to reduce words to their base form.

==II. Model Initialization==
;A. Embedding Layer Initialization
* The LLM model is initialized with its pre-trained '''weights''' and '''biases''', which are typically stored in a '''weight matrix'''.
* The embedding layer, responsible for mapping input tokens to dense vector representations, is initialized with pre-trained '''embeddings''' or randomly initialized '''parameters'''.

==III. Contextualization==
;A. Attention Mechanisms
* The LLM model uses various attention mechanisms, such as self-attention or multi-head attention, to weigh the importance of different input tokens.
* The output of the contextualization process is a dense vector representation of the input, which captures its semantic meaning.

==IV. Response Generation==
;A. Sampling and Decoding
* The LLM model generates a response by sampling from the output distribution of the final layer, using techniques such as greedy search or beam search.
* The sampled output is then decoded into a sequence of tokens, which form the generated response.

==V. Post-processing==
;A. Output Formatting
* The generated response may undergo various post-processing steps, such as tokenization or stopword removal, to produce a final output.
* The output is then returned to the user, who can interact with it using the system's interface.
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