Organizations are increasingly faced with the decision of whether to implement Retrieval-Augmented Generation (RAG) or fine-tuning for their AI initiatives. RAG connects large language models to external databases, allowing access to real-time information, reducing inaccuracies, and enhancing security and traceability. However, implementing RAG comes with its own technical challenges that require careful planning and maintenance.

• RAG connects LLMs to external data sources so responses reflect real-time information rather than static training data
• Grounding responses in retrieved data cuts down on hallucinations and improves accuracy
• RAG offers added benefits like source traceability and better security for proprietary data
• Setting up RAG requires substantial infrastructure work—data pipelines, indexing, and ongoing maintenance for quality and scalability