Prompt Augmentation

Prompt Augmentation is the practice of enriching a large-language-model prompt with extra context—retrieved documents, user profile data, metadata tags, or chain-of-thought hints—to boost relevance, factuality, or style control. Instead of sending a bare question, developers prepend or append snippets selected by a retriever, template variables, or function-call schemas. Common techniques include vector-based context insertion in Retrieval-Augmented Generation (RAG), few-shot exemplars, system-level instructions, and dynamic variables like current date or brand tone. The augmented prompt fits inside the model’s context window, reducing hallucinations and token waste while preserving latency. Key tuning knobs are chunk size, Top-k selection, and max-marginal-relevance (MMR) to avoid redundancy. Metrics such as exact-answer rate and prompt-cost ratio measure gains. By treating the prompt as a programmable interface, Prompt Augmentation turns a generic LLM into a domain-aware copilot that answers accurately and speaks in the desired voice.