In this infodemic era, fact-checking is becoming a vital task. However, it is a complex and time-consuming activity. In this talk, we will see how to combine Information Retrieval tools with modern Language Models to simply implement a fact-checking baseline with low human effort. I will show you how to build a funny use case around rock music. The application is based on several Python open-source libraries: Haystack, FAISS, Hugging Face Transformers, Sentence Transformers. This step-by-step implementation will be an opportunity to learn more about Dense retrieval and Natural Language Inference models in a hands-on way. I will share some insights into developing modern Natural Language applications. **Why it's relevant:** Fact-checking is significant to the society, although it is still difficult to do automatically. Using modern NLP tools can help speed up and automate part of this task. **What the audience will learn:** - Dense retrieval for semantic search - Natural Language Inference models - How to build a fact-checking system using Haystack, FAISS, Hugging Face Transformers, Sentence Transformers. - How to integrate powerful (Large) Language Models in your NLP applications, conditioning them to operate on your knowledge base - How to efficiently combine tools from Information Retrieval, NLP, and Vector Search