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Augmented Prompt

An augmented prompt is the final input payload sent to a Large Language Model (LLM) that fuses the user's original query with external, retrieved context to ensure the model's response is grounded in factual, domain-specific data. The primary architectural trade-off involves balancing context density for accuracy against increased inference latency and potential 'lost-in-the-middle' performance degradation as the prompt nears the context window limit.

Definition

An augmented prompt is the final input payload sent to a Large Language Model (LLM) that fuses the user's original query with external, retrieved context to ensure the model's response is grounded in factual, domain-specific data. The primary architectural trade-off involves balancing context density for accuracy against increased inference latency and potential 'lost-in-the-middle' performance degradation as the prompt nears the context window limit.

Disambiguation

It is the specific output of the 'Augmentation' phase in RAG, distinct from static prompt engineering or system messages.

Visual Metaphor

"An open-book exam where the student is handed the specific textbook page relevant to the question just before they answer."

Key Tools
LangChain (PromptTemplates)LlamaIndexDSPyHaystack
Related Connections

Conceptual Overview

An augmented prompt is the final input payload sent to a Large Language Model (LLM) that fuses the user's original query with external, retrieved context to ensure the model's response is grounded in factual, domain-specific data. The primary architectural trade-off involves balancing context density for accuracy against increased inference latency and potential 'lost-in-the-middle' performance degradation as the prompt nears the context window limit.

Disambiguation

It is the specific output of the 'Augmentation' phase in RAG, distinct from static prompt engineering or system messages.

Visual Analog

An open-book exam where the student is handed the specific textbook page relevant to the question just before they answer.

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