Retrieval augmented generation best practices

Retrieval augmented generation, or RAG, is becoming a revolutionary force in the ever-evolving field of artificial intelligence and generative artificial intelligence (GenAI)

RAG is enabling scalability, promoting innovation, facilitating real-time data access, and democratizing AI

Generative AI models have historically been restricted to their training set. A data scientist is needed for any adjustments or fine-tuning, and they can be highly expensive and hard to come by

Users can customize RAG to access and use a variety of external data sources to meet business needs in various industries

RAG makes it easier to fine-tune AI models, resulting in a reduction in resource usage and an increase in user friendliness

RAG is transforming the way businesses use generative AI to introduce innovation into their daily operations

RAG enables improved efficiency and scalability without requiring the model to be retrained by providing extra data to the large language models

RAG helps LLMs comprehend the context of a request by presenting pertinent documents that have been retrieved in addition to user queries