glossary changedabout-claude/glossary
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## RAG (retrieval augmented generation) ## TTFT (time to first token) ## RAG (Retrieval augmented generation) ## TTFT (Time to first token)
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77## Context window
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9The "context window" refers to the amount of text a language model can look back on and reference when generating new text. This is different from the large corpus of data the language model was trained on, and instead represents a "working memory" for the model. A larger context window allows the model to process and respond to more complex and lengthy prompts, while a smaller context window may limit the model's ability to handle longer prompts or maintain coherence over extended conversations.
9The "context window" refers to the amount of text a language model can look back on and reference when generating new text. This is different from the large corpus of data the language model was trained on, and instead represents a "working memory" for the model. A larger context window allows the model to process and respond to more complex and lengthy prompts, whereas a smaller context window might limit the model's ability to handle longer prompts or maintain coherence over extended conversations.
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1111See the [guide to understanding context windows](https://platform.claude.com/docs/en/build-with-claude/context-windows) to learn more.
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3333## MCP (Model Context Protocol)
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35Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. Like a USB-C port for AI applications, MCP provides a unified way to connect AI models to different data sources and tools. MCP enables AI systems to maintain consistent context across interactions and access external resources in a standardized manner. See the [MCP documentation](https://modelcontextprotocol.io/) to learn more.
35Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. Like a USB-C port for AI applications, MCP provides a unified way to connect AI models to different data sources and tools. MCP allows AI systems to maintain consistent context across interactions and access external resources in a standardized manner. See the [MCP documentation](https://modelcontextprotocol.io/) to learn more.
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3737## MCP connector
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39The MCP connector is a feature that allows API users to connect to MCP servers directly from the Messages API without building an MCP client. This enables seamless integration with MCP-compatible tools and services through the Claude API. The MCP connector supports features such as tool calling and is available in beta. See the [MCP connector](https://platform.claude.com/docs/en/agents-and-tools/mcp-connector) documentation to learn more.
39The MCP connector is a feature that allows API users to connect to MCP servers directly from the Messages API without building an MCP client. This allows seamless integration with MCP-compatible tools and services through the Claude API. The MCP connector supports features such as tool calling and is available in beta. See the [MCP connector](https://platform.claude.com/docs/en/agents-and-tools/mcp-connector) documentation to learn more.
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4141## Pretraining
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43Pretraining is the initial process of training language models on a large unlabeled corpus of text. In Claude's case, autoregressive language models (such as Claude's underlying model) are pretrained to predict the next word, given the previous context of text in the document. These pretrained models are not inherently good at answering questions or following instructions, and often require deep skill in prompt engineering to elicit desired behaviors. Fine-tuning and RLHF are used to refine these pretrained models, making them more useful for a wide range of tasks.
43Pretraining is the initial process of training language models on a large unlabeled corpus of text. In Claude's case, autoregressive language models (such as Claude's underlying model) are pretrained to predict the next word, given the previous context of text in the document. These pretrained models are not inherently good at answering questions or following instructions, and often require deep skill in prompt engineering to elicit the behaviors you want. Fine-tuning and RLHF are used to refine these pretrained models, making them more useful for a wide range of tasks.
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45## RAG (Retrieval augmented generation)
45## RAG (retrieval augmented generation)
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4747Retrieval augmented generation (RAG) is a technique that combines information retrieval with language model generation to improve the accuracy and relevance of the generated text, and to better ground the model's response in evidence. In RAG, a language model is augmented with an external knowledge base or a set of documents that is passed into the context window. The data is retrieved at runtime when a query is sent to the model, although the model itself does not necessarily retrieve the data (but can with [tool use](https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview) and a retrieval function). When generating text, relevant information first must be retrieved from the knowledge base based on the input prompt, and then passed to the model along with the original query. The model uses this information to guide the output it generates. This allows the model to access and use information beyond its training data, reducing the reliance on memorization and improving the factual accuracy of the generated text. RAG can be particularly useful for tasks that require up-to-date information, domain-specific knowledge, or explicit citation of sources. However, the effectiveness of RAG depends on the quality and relevance of the external knowledge base and the knowledge that is retrieved at runtime.
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4949## RLHF
5050
51Reinforcement Learning from Human Feedback (RLHF) is a technique used to train a pretrained language model to behave in ways that are consistent with human preferences. This can include helping the model follow instructions more effectively or act more like a chatbot. Human feedback consists of ranking a set of two or more example texts, and the reinforcement learning process encourages the model to prefer outputs that are similar to the higher-ranked ones. Claude has been trained using RLHF to be a more helpful assistant. For more details, you can read [Anthropic's paper on the subject](https://arxiv.org/abs/2204.05862).
51Reinforcement learning from human feedback (RLHF) is a technique used to train a pretrained language model to behave in ways that are consistent with human preferences. This can include helping the model follow instructions more effectively or act more like a chatbot. Human feedback consists of ranking a set of two or more example texts, and the reinforcement learning process encourages the model to prefer outputs that are similar to the higher-ranked ones. Claude has been trained using RLHF to be a more helpful assistant. For more details, you can read Anthropic's [paper on RLHF](https://arxiv.org/abs/2204.05862).
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5353## Temperature
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55Temperature is a parameter that controls the randomness of a model's predictions during text generation. Higher temperatures lead to more creative and diverse outputs, allowing for multiple variations in phrasing and, in the case of fiction, variation in answers as well. Lower temperatures result in more conservative and deterministic outputs that stick to the most probable phrasing and answers. Adjusting the temperature enables users to encourage a language model to explore rare, uncommon, or surprising word choices and sequences, rather than only selecting the most likely predictions.
55Temperature is a parameter that controls the randomness of a model's predictions during text generation. Higher temperatures lead to more creative and diverse outputs, allowing for multiple variations in phrasing and, in the case of fiction, variation in answers as well. Lower temperatures result in more conservative and deterministic outputs that stick to the most probable phrasing and answers. Adjusting the temperature allows users to encourage a language model to explore rare, uncommon, or surprising word choices and sequences, rather than only selecting the most likely predictions.
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57Users may encounter non-determinism in APIs. Even with temperature set to 0, the results will not be fully deterministic and identical inputs may produce different outputs across API calls. This applies both to Anthropic's first-party inference service and to inference through third-party cloud providers.
57Users might encounter non-determinism in APIs. Even with temperature set to 0, the results will not be fully deterministic, and identical inputs might produce different outputs across API calls. This applies both to Anthropic's first-party inference service and to inference through third-party cloud providers.
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59## TTFT (Time to first token)
59## TTFT (time to first token)
6060
61Time to First Token (TTFT) is a performance metric that measures the time it takes for a language model to generate the first token of its output after receiving a prompt. It is an important indicator of the model's responsiveness and is particularly relevant for interactive applications, chatbots, and real-time systems where users expect quick initial feedback. A lower TTFT indicates that the model can start generating a response faster, providing a more seamless and engaging user experience. Factors that can influence TTFT include model size, hardware capabilities, network conditions, and the complexity of the prompt.
61Time to first token (TTFT) is a performance metric that measures the time it takes for a language model to generate the first token of its output after receiving a prompt. It is an important indicator of the model's responsiveness and is particularly relevant for interactive applications, chatbots, and real-time systems where users expect quick initial feedback. A lower TTFT indicates that the model can start generating a response faster, providing a more seamless and engaging user experience. Factors that can influence TTFT include model size, hardware capabilities, network conditions, and the complexity of the prompt.
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6363## Tokens
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65Tokens are the smallest individual units of a language model, and can correspond to words, subwords, characters, or even bytes (in the case of Unicode). For Claude, the number of characters a token represents depends on the model and the language. Claude 4.7 and later models and Claude Mythos Preview use a newer tokenizer that produces approximately 30 percent more tokens for the same text than earlier models, on which a token represents approximately 3.5 English characters. To get exact counts, use [token counting](https://platform.claude.com/docs/en/build-with-claude/token-counting) with the model you plan to use. Tokens are typically hidden when interacting with language models at the "text" level but become relevant when examining the exact inputs and outputs of a language model. When Claude is provided with text to evaluate, the text (consisting of a series of characters) is encoded into a series of tokens for the model to process. Larger tokens enable data efficiency during inference and pretraining (and are used when possible), while smaller tokens allow a model to handle uncommon or never-before-seen words. The choice of tokenization method can impact the model's performance, vocabulary size, and ability to handle out-of-vocabulary words.
65Tokens are the smallest individual units of a language model, and can correspond to words, subwords, characters, or even bytes (in the case of Unicode). For Claude, the number of characters a token represents depends on the model and the language. Claude 4.7 and later models and Claude Mythos Preview use a newer tokenizer that produces approximately 30 percent more tokens for the same text than earlier models, on which a token represents approximately 3.5 English characters. To get exact counts, use [token counting](https://platform.claude.com/docs/en/build-with-claude/token-counting) with the model you plan to use. Tokens are typically hidden when interacting with language models at the "text" level but become relevant when examining the exact inputs and outputs of a language model. When Claude is provided with text to evaluate, the text (consisting of a series of characters) is encoded into a series of tokens for the model to process. Larger tokens improve data efficiency during inference and pretraining (and are used when possible), whereas smaller tokens allow a model to handle uncommon or never-before-seen words. The choice of tokenization method can impact the model's performance, vocabulary size, and ability to handle out-of-vocabulary words.
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