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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 R
10 termsRandom seed
Random seed 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 fundamentalsReasoning trace
Reasoning trace 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 fundamentalsReinforcement learning from AI feedback
Reinforcement learning from AI feedback 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 fundamentalsRelative positional encoding
Relative positional encoding 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 fundamentalsRepetition control
Repetition control 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 fundamentalsResidual connection
Residual connection 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 fundamentalsResponse prefilling
Response prefilling 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 fundamentalsRetrieval augmented generation
Retrieval augmented generation, or RAG, is an architecture that retrieves relevant documents or records and supplies them to a language model as context for generating a response. It can ground answers in a changing or private information source without retraining the model for every update.
LLM fundamentalsReward model
Reward model 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 fundamentalsRotary positional embedding
Rotary positional embedding 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 fundamentals