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-==Tokens==
+==Text Fragments==
-Tokens are fragments of words, which may include trailing spaces or sub-words. They are used by natural language processing (NLP) systems, such as the OpenAI API, to process text input. The way words are broken down into tokens is language-dependent, which can affect the implementation cost of the API for languages other than English.
+Text fragments, often called tokens, are parts of words that can include spaces at the end or even smaller components of words. Natural language processing (NLP) systems, like the OpenAI API, use tokens to manage and process textual input. The method in which words are split into tokens relies on the language, which may impact the cost of implementing the API for languages other than English.
-===Understanding Token Lengths===
+===Grasping Fragment Lengths===
-To grasp the concept of tokens, consider the following generalizations about token lengths:
+To comprehend the idea of tokens, consider these general approximations concerning token lengths:
 token ≈ 4 characters in English
 token ≈ ¾ words
 tokens ≈ 75 words
-In terms of sentences and paragraphs:
+Regarding sentences and paragraphs:
 -2 sentences ≈ 30 tokens
 paragraph ≈ 100 tokens
 ,500 words ≈ 2048 tokens
-For additional context, consider the following examples:
+For more context, consider these examples:
 Wayne Gretzky’s quote "You miss 100% of the shots you don't take" contains 11 tokens.
 OpenAI’s charter contains 476 tokens.
 The transcript of the US Declaration of Independence contains 1,695 tokens.
-===Tokenization Tools===
+===Fragmentation Tools===
-To further explore tokenization, the following tools and libraries can be used:
+To delve deeper into tokenization, the following tools and libraries are available:
 [[OpenAI]]'s interactive Tokenizer tool
-[[Tiktoken]], a fast BPE tokenizer specifically for OpenAI models
+[[Tiktoken]], a swift BPE tokenizer designed for OpenAI models
 Transformers package for Python
 gpt-3-encoder package for node.js
-==Token Limits==
+==Fragment Limits==
-The token limit for requests depends on the model used, with a maximum of 4097 tokens shared between prompt and completion. If a prompt has 4000 tokens, the completion can only have up to 97 tokens. This limit is a technical constraint, but there are ways to work within it, such as condensing prompts or breaking text into smaller parts.
+The fragment limit for requests is contingent on the model employed, with a maximum of 4097 tokens shared between the prompt and its completion. If a prompt consists of 4000 tokens, the completion can have a maximum of 97 tokens. This limitation is a technical constraint, but there are strategies to work within it, such as shortening prompts or dividing text into smaller sections.
-==Token Pricing==
+==Fragment Pricing==
-API token pricing varies depending on the model type, with different capabilities and speeds offered at different price points. Davinci is the most capable model, while Ada is the fastest. Detailed token pricing information can be found on the OpenAI API's pricing page.
+API fragment pricing differs depending on the model type, with various capabilities and speeds available at distinct price points. Davinci is the most proficient model, while Ada is the swiftest. Detailed fragment pricing information can be found on the OpenAI API's pricing page.
-==Token Context==
+==Fragment Context==
-GPT-3 processes tokens based on their context in the corpus data. Identical words may have different tokens depending on their structure within the text. For instance, the token value generated for the word "red" varies based on its context.
+GPT-3 handles tokens based on their context in the corpus data. Identical words might have different tokens depending on their arrangement within the text. For instance, the token value generated for the word "red" changes based on its context.
 Lowercase in the middle of a sentence: " red" (token: "2266")
 Uppercase in the middle of a sentence: " Red" (token: "2297")
 Uppercase at the beginning of a sentence: "Red" (token: "7738")
-The more probable or frequent a token is, the lower the token number assigned to it. For example, the token for the period ("13") remains the same in all three sentences because its usage is similar across the corpus data.
+The more likely or common a token is, the lower the token number assigned to it. For example, the token for the period ("13") remains consistent in all three sentences because its usage is similar throughout the corpus data.
-==Prompt Design and Token Knowledge==
+==Designing Prompts with Fragment Knowledge==
-Understanding tokens can help improve prompt design in several ways.
+Understanding tokens can help enhance prompt design in several ways.
 ===Prompts Ending with a Space===
-Since tokens can include trailing space characters, prompts ending with a space may produce lower-quality output. The API's token dictionary already accounts for trailing spaces.
+As tokens can include spaces at the end, prompts that end with a space might produce suboptimal output. The API's token dictionary already considers trailing spaces.
 ===Using the logit_bias Parameter===
-The logit_bias parameter allows setting biases for specific tokens, modifying the likelihood of those tokens appearing in the completion. For example, if creating an AI Baking Assistant that considers users' egg allergies, the logit_bias parameter can be used to discourage the model from generating responses that include any variation of the word "egg".
+The logit_bias parameter enables setting biases for specific tokens, altering the probability of those tokens appearing in the completion. For example, if designing an AI Baking Assistant that takes users' egg allergies into account, the logit_bias parameter can be used to deter the model from generating responses that include any form of the word "egg".
 First, use a tokenizer tool to identify the tokens for which biases should be set:
-Singular with trailing space: " egg" (token: "5935")
+Singular with trailing space: " egg"(token: "5935")
 Plural with trailing space: " eggs" (token: "9653")
 Subword token generated for "Egg" or "Eggs": "gg" (token: "1130")
-The logit_bias parameter accepts bias values ranging from -100 to +100. Extreme values result in either a ban (-100) or exclusive selection (100) of the relevant token.
+The logit_bias parameter accepts bias values ranging from -100 to +100. Extreme values result in either prohibiting (-100) or exclusively selecting (100) the related token.
-By adding the logit biases to the prompt, the likelihood of the word "egg" (and its variations) appearing in the response for a banana bread recipe is reduced. As a result, the AI Baking Assistant generates a response that does not include eggs, fulfilling its requirement of considering the user's egg allergies.
+By incorporating the logit biases into the prompt, the likelihood of the word "egg" (and its variations) appearing in the response for a banana bread recipe is diminished. Consequently, the AI Baking Assistant generates a response that excludes eggs, meeting its requirement of being mindful of the user's egg allergies.