Define the new internet.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
1,322 definitions
機械支援の翻訳下書き (Japanese) for "Embeddings": Dense numerical vector representations of words, sentences, or other data that capture semantic meaning. Similar concepts have similar embeddings (nearby in vector space), allowing AI systems to measure meaning similarity mathematically rather than relying on exact keyword matches.
“例文の下書き: The search engine uses embeddings to find relevant results even when the query words don't appear in the document.”
機械支援の翻訳下書き (Japanese) for "Vector Database": A specialized database optimized for storing and querying high-dimensional vector embeddings. Vector databases power semantic search, recommendation systems, and RAG architectures by efficiently finding the most similar vectors to a given query.
“例文の下書き: We stored all our documentation as embeddings in a vector database so the AI could find relevant passages instantly.”
機械支援の翻訳下書き (Japanese) for "Tool Calling": A capability that allows language models to invoke external functions, APIs, or services during generation. The model decides when to call a tool, formats the call arguments as JSON, receives the result, and incorporates it into its response — enabling real-world action beyond text generation.
“例文の下書き: The agent used tool calling to check the current weather before generating its travel recommendations.”
機械支援の翻訳下書き (Japanese) for "Multimodal": Describing AI systems capable of processing and generating multiple types of data — such as text, images, audio, and video — in a unified model. Multimodal AI can answer questions about images, generate images from text, transcribe speech, and reason across modalities simultaneously.
“例文の下書き: The multimodal model analyzed the chart image and provided a written summary of the trends.”
機械支援の翻訳下書き (Japanese) for "Chain of Thought": A prompting technique where a language model is encouraged or required to show its step-by-step reasoning before providing a final answer. Chain-of-thought prompting significantly improves accuracy on complex tasks like math, logic puzzles, and multi-step planning.
“例文の下書き: Adding "let's think step by step" to the prompt triggered chain-of-thought reasoning and doubled accuracy.”
機械支援の翻訳下書き (Japanese) for "Zero-Shot": The ability of a model to perform a task it has never been explicitly trained or shown examples for. Zero-shot learning relies on the model's generalized understanding from pretraining to handle novel tasks based on instruction alone.
“例文の下書き: The model classified customer sentiment zero-shot without any labeled training examples.”
機械支援の翻訳下書き (Japanese) for "Few-Shot": A prompting approach where a small number of input-output examples are included in the context to guide model behavior on a new task. Few-shot prompting helps models understand the desired format, tone, or logic without any weight updates.
“例文の下書き: We gave the model three few-shot examples of our data format and it immediately understood the pattern.”
機械支援の翻訳下書き (Japanese) for "Grounding": The process of connecting an AI model's outputs to verified, real-world information sources. Grounding reduces hallucination by anchoring responses to retrieved documents, databases, or live data rather than relying purely on the model's learned parameters.
“例文の下書き: Grounding the chatbot in our product database eliminated the fabricated feature claims.”
機械支援の翻訳下書き (Japanese) for "Inference": The act of running a trained machine learning model on new input data to generate predictions or outputs. Inference is distinct from training — it is the "serving" phase where the model is used in production, and its speed and cost are critical for real-world applications.
“例文の下書き: Inference latency dropped from 2 seconds to 200ms after switching to a quantized model.”
機械支援の翻訳下書き (Japanese) for "Tokenization": The process of converting raw text into discrete units called tokens that a language model can process. Tokens are typically subword units — common words become single tokens while rare words split into multiple tokens. All LLM pricing and context limits are measured in tokens, not characters or words.
“例文の下書き: The word "unbelievable" tokenized into three pieces: "un", "believ", "able".”