PRIME: Prompt Refinement via Information-driven Methods and Expansion, A Modular Framework for Context-Aware Prompt Amplification

While Large Language Models (LLMs) have transformed natural language processing, their effectiveness depends critically on prompt quality. Current Retrieval-Augmented Generation (RAG) systems retrieve documents to generate answers; we propose a fundamentally different approach: using retrieval to construct better questions. This paper introduces PRIME (Prompt Refinement via Information-driven Methods and Expansion), a framework that treats prompt construction as a first-class optimization problem. PRIME implements a modular pipeline with heterogeneous document loaders (10+ formats), pluggable embedding strategies (sparse TF-IDF/BM25 and dense Sentence-BERT/Gemini-Embedding-004/OpenAI), persistent vector stores, and multi-provider LLM generators (GPT-4o, Claude-3, Gemini-2.5-Flash, Gemini-3-Flash-Preview). We formalize prompt amplification mathematically and introduce four novel evaluation metrics: structural coherence (S), semantic specificity (P), contextual completeness (C), and lexical readability (L). Comprehensive experiments across four domains, 12 embedding configurations, 6 LLM backends, and 30 human-evaluated prompts reveal: (1) dense embeddings achieve 37-73% higher retrieval precision; (2) Gemini-3-Flash-Preview achieves 165x expansion ratio with 0.798 quality score; (3) complex queries outperform simple ones by 92%; (4) human evaluators rate PRIME outputs 4.2/5 with 87% inter-rater agreement; and (5) caching provides 1,944x speedup. We present systematic failure analysis identifying when amplification degrades performance. PRIME is released as an open-source library (pip install prompt-amplifier).