Retrieval-augmented generation (RAG) has emerged as a powerful approach in machine learning, enhancing the capabilities of AI systems. However, it is essential to recognize the vulnerabilities associated with vector and embedding methods.
One significant concern is that updates to knowledge bases can introduce new documents that may not align with the existing AI model's understanding, potentially leading to inaccuracies in responses.
By examining these weaknesses, we can better prepare for the challenges posed by evolving data and improve the reliability of AI applications.