Shelf Blog: RAG

When a company implements enterprise RAG, it has perfectly understandable expectations that the AI will provide accurate and up-to-date answers based on corporate knowledge. But instead, the company sees confident yet incorrect answers, inconsistencies across interactions, and reliance on outdated...

Large language models are confident, eloquent, and often get questions about your business wrong. The reason is that they weren’t trained on your data. They know the world in general, but they don’t know your return policy, your product specifications, or the current terms of your contracts....

Large language models have an impressive ability to generate human-like content, but they also run the risk of generating confusing or inaccurate responses. In some cases, LLM responses can be harmful, biased, or even nonsensical. The cause? Poor data quality.  According to a poll of IT leaders by...

What is Retrieval-Augmented Generation? Retrieval-Augmented Generation (RAG) is a Generative AI (GenAI) implementation technique that is accelerating the adoption of GenAI and Large Language Models (LLMs) across enterprise environments. By enabling organizations to use their proprietary data in...

Retrieval-augmented generation (RAG) is an innovative technique in natural language processing that combines the power of retrieval-based methods with the generative capabilities of large language models. By integrating real-time, relevant information from various sources into the generation...

As the use of Retrieval-Augmented Generation (RAG) systems becomes more common in countless industries, ensuring their performance and fairness has become more critical than ever. RAG systems, which enhance content generation by integrating retrieval mechanisms, are powerful tools to improve...