Leveraging transformer models can help users find answers in FAQs by understanding the intent behind queries. Traditional keyword-based search systems are limited, as they require exact word matches, which can be challenging for users who use different wording to ask questions. Transformer-based language models like BERT and RoBERTa can go beyond literal matching and grasp the meaning behind a query, making them suitable for question answering tasks. A semantic FAQ search system uses dense retrieval models, such as Sentence-transformers, to represent FAQs as vectors in a high-dimensional embedding space, allowing the system to compare queries to questions and return answer associated with that question. An example implementation using Haystack demonstrates how to build an FAQ pipeline on top of a small dataset of FAQs about chocolate, showcasing how the system can match queries to relevant answers despite minor differences in wording.