The hallucinations of large language models are mainly a result of deficiencies in the dataset and training. These can be mitigated with retrieval-augmented generation and real-time data. Artificial ...

Punnam Raju Manthena, Co-Founder & CEO at Tekskills Inc. Partnering with clients across the globe in their digital transformation journeys. Retrieval-augmented generation (RAG) is a technique for ...

Rahul is the Chief Product and Marketing Officer for Innodata, a global data engineering company powering next-generation AI applications. Generative AI is transforming industries and lives. It ...

RAG is a pragmatic and effective approach to using large language models in the enterprise. Learn how it works, why we need it, and how to implement it with OpenAI and LangChain. Typically, the use of ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Today’s large language models (LLMs) are increasingly complex, but often, ...

Retrieval-augmented generation is enhancing large language models' accuracy and specificity. However, it still poses challenges and requires specific implementation techniques. This article is part of ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

Retrieval-Augmented Generation (RAG) systems have emerged as a powerful approach to significantly enhance the capabilities of language models. By seamlessly integrating document retrieval with text ...

COMMISSIONED: Retrieval-augmented generation (RAG) has become the gold standard for helping businesses refine their large language model (LLM) results with corporate data. Whereas LLMs are typically ...

Widespread amazement at Large Language Models' capacity to produce human-like language, create code, and solve complicated problems characterized ...