Imagine a world where medical jargon is no longer a barrier between doctors and patients. Recent research explores how small language models (SLMs), the more compact cousins of the AI giants like ChatGPT, can be harnessed to translate complex medical terms into easy-to-understand language. This innovative approach, dubbed "pRAGe" (pipeline for Retrieval Augmented Generation and evaluation), combines the power of retrieval augmented generation with SLMs to provide clear, concise paraphrases and definitions. Why is this important? Traditional large language models, while impressive, are computationally expensive and prone to "hallucinations," generating incorrect or nonsensical information. This is especially risky in the medical field, where accuracy is paramount. pRAGe tackles these challenges by using smaller, more efficient models and grounding their responses in a reliable medical knowledge base. This research used French medical texts as a case study, training the system on a unique dataset of medical terms and their simplified paraphrases. The results were encouraging, demonstrating the potential of pRAGe to improve patient understanding and communication. The researchers fine-tuned the models, leading to more concise and accurate paraphrases, effectively bridging the gap between medical jargon and everyday language. For example, the complex term "osteophyte" was simplified to "deposits of bone tissue that form on the edges of bones." This approach, while still in its early stages, offers a promising solution to the persistent problem of medical jargon. Further research aims to expand language support and refine the knowledge base, ultimately paving the way for a future where AI empowers patients to take a more active role in their healthcare through clearer understanding of medical terminology.