Shelf Blog: RAG

Most teams that set out to address low RAG accuracy start by opening the prompt. Many begin by changing the wording of the system prompt. Some try a different model and add stricter instructions. But both groups get the same result: the answers are still inaccurate. However, the real issue is that...

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 But instead, the company sees confident yet incorrect answers, inconsistencies across interactions, and reliance on outdated...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...