Large language models (LLMs) are impressive at generating human-like text, but they can sometimes provide outdated or inaccurate information because their training data is static. This is especially problematic in fields with rapidly changing knowledge, like healthcare or finance. Retrieval Augmented Generation (RAG) offers a powerful solution. RAG systems connect LLMs to external data sources like PDFs, databases, or websites, grounding their responses in up-to-date, accurate information. This post provides a practical guide to building your own RAG system using PDFs, offering both proprietary and open-source approaches. Traditional LLMs like GPT, BERT, or T5 are limited by their knowledge cut-off date. They excel at general tasks but struggle in specialized areas requiring up-to-the-minute data. RAG changes this dynamic by incorporating real-time retrieval from external sources. This works by first taking a user's query and transforming it into a vector embedding, a numerical representation capturing its semantic meaning. This vector is then compared to embeddings of chunks of text from your document database (like a collection of PDFs). The most similar chunks are retrieved and fed to the LLM along with the original query, enabling the model to give a response based on current and relevant data. PDFs are particularly valuable for RAG due to their widespread use in sharing important information. They contain rich, structured data often found in research, legal, technical, and financial documents. While PDFs are a goldmine of information, they also pose challenges. Their complex layouts, diverse formatting, and the presence of non-textual elements like charts and images can make extracting clean text difficult. This requires sophisticated extraction tools and strategies, as text accuracy is crucial for high-performing RAG systems. This guide provides a comprehensive step-by-step approach to build a RAG system: data preparation, embedding creation, retrieval, and response generation. It emphasizes two primary approaches: OpenAI's Assistant API and the open-source Llama model. OpenAI provides an easy-to-use, cloud-based solution ideal for quick prototyping and deployment. Llama offers more control and customization, allowing developers to fine-tune the model to specific domains and deploy it in diverse environments. The OpenAI path involves setting up the environment, configuring the API, preparing your PDFs, creating a vector store, designing an assistant with specific instructions, and finally initiating a conversation. The Llama approach leverages tools like Ollama, FAISS for indexing, and Langchain to create a powerful local RAG system. Preliminary evaluations show that the RAG approach significantly improves response accuracy and reliability. Participants in a workshop who used this guide were able to successfully implement RAG systems and reported a deeper understanding of the process. However, challenges remain, especially concerning the handling of diverse PDF formats and the computational resources required for large-scale deployments. Future research focuses on enhancing adaptability, cross-lingual capabilities, and integration with knowledge graphs to make RAG systems even more powerful. As RAG systems evolve, they promise to revolutionize how we access and utilize information, opening up exciting new possibilities across numerous fields.