Find a term
Build a practical understanding of language models, prompts, tokens, and context. Focus on the concepts that affect real applications and engineering workflows.
Terms beginning with T
14 termsTask decomposition prompting
Task decomposition prompting is a language-model concept about representation and similarity. It names a mechanism, representation, prompting pattern, decoding control, or context behavior that can change how an AI system processes input and produces output.
LLM fundamentalsTemperature
Temperature is a generation parameter that changes how a language model samples from the probability distribution of possible next tokens. Higher values generally allow more variation, while lower values generally favor higher-probability choices.
LLM fundamentalsTemperature scaling
Temperature scaling is a language-model concept about instruction design and control. It names a mechanism, representation, prompting pattern, decoding control, or context behavior that can change how an AI system processes input and produces output.
LLM fundamentalsToken embedding
Token embedding is a language-model concept about generation behavior and sampling. It names a mechanism, representation, prompting pattern, decoding control, or context behavior that can change how an AI system processes input and produces output.
LLM fundamentalsTokenization
Tokenization is the process of converting text into the token units a language model receives and generates. A token can represent a word, part of a word, punctuation, or another piece of text, depending on the tokenizer.
LLM fundamentalsTop-k sampling
Top-k sampling is a language-model concept about representation and similarity. It names a mechanism, representation, prompting pattern, decoding control, or context behavior that can change how an AI system processes input and produces output.
LLM fundamentalsTop-p sampling
Top-p sampling, also called nucleus sampling, selects the next token from the smallest group whose cumulative probability reaches a chosen probability p. Lower p values restrict the candidate set, while higher values allow more candidates.
LLM fundamentalsTraining batch size
Batch size is a language-model concept about serving behavior and operational tradeoffs. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.
LLM fundamentalsTraining data contamination
Training data contamination is a language-model concept about training behavior and measurement. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.
LLM fundamentalsTraining loss
Training loss is a language-model concept about training behavior and measurement. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.
LLM fundamentalsTraining step
Training step is a language-model concept about training behavior and measurement. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.
LLM fundamentalsTransformer block
Transformer block is a language-model concept about mechanism and information flow. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.
LLM fundamentalsTree of thoughts
Tree of thoughts is a language-model concept about instruction design and control. It names a mechanism, representation, prompting pattern, decoding control, or context behavior that can change how an AI system processes input and produces output.
LLM fundamentalsTypical sampling
Typical sampling is a language-model concept about context selection and limits. It names a mechanism, representation, prompting pattern, decoding control, or context behavior that can change how an AI system processes input and produces output.
LLM fundamentals