The article discusses the rise of retrieval augmented generation (RAG) architectures among large language model (LLM) publishers, emphasizing the need for accuracy in AI responses. While generative AI alone can produce hallucinated results, RAG aims to enhance response reliability by leveraging verified knowledge bases. The operational framework of RAG involves comparing user prompts with relevant data sources to generate accurate results. However, data preparation is crucial, and firms must strategize on document ingestion and system updates to avoid failures in response accuracy and relevance.
All the large language model (LLM) publishers and suppliers are focusing on the advent of artificial intelligence (AI) agents and agentic AI.
Augmented generation through retrieval enables the results of a generative AI model to be anchored in truth.
What is RAG architecture? Initial representations of RAG architectures do not shed any light on the essential workings of these systems.
Failures come from the question of how to prepare data and the methods used in transforming it.
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