RAG

. We’ve all heard or experienced it. Natural Language Generation models can sometimes hallucinate, i.e., they begin generating text that just isn't quite accurate for the prompt provided. In layman’s terms, they begin ...

As Artificial Intelligence (AI) continues to dominate headlines, the main target of conversation is shifting to the outcomes and implications for businesses. Many large enterprises are using AI to automate repetitive tasks, like accounting,...

, legal contracts are foundational documents that outline the relationships, obligations, and responsibilities between parties. Whether it’s a partnership agreement, an NDA, or a supplier contract, these documents often contain critical information that drives...

Retrieval-Augmented Generation (RAG) is an approach to constructing AI systems that mixes a language model with an external knowledge source. In easy terms, the AI first searches for relevant documents (like articles or webpages)...

Introduction Many generative AI use cases still revolve around Retrieval Augmented Generation (RAG), yet consistently fall wanting user expectations. Despite the growing body of research on RAG improvements and even adding Agents into the method,...

For years, search engines like google and yahoo and databases relied on essential keyword matching, often resulting in fragmented and context-lacking results. The introduction of generative AI and the emergence of Retrieval-Augmented Generation (RAG)...

Artificial Intelligence (AI) Startup Contextual AI has launched a brand new large language model (LLM) that minimizes hallucinations based on 'RAG 2.0' technology, which has reorganized search augmentation (RAG). Created by the founding father...

Retrieval-Augmented Generation (RAG) is a robust technique that enhances language models by incorporating external information retrieval mechanisms. While standard RAG implementations improve response relevance, they often struggle in complex retrieval scenarios. This text explores...