Retrieval-Augmented Generation: A More Reliable Approach

In the rapidly changing world of artificial intelligence, it has evolved far more than just predictions based on data analysis. It is now emerging with limitless potential for generating creative content and problem-solving models. With generative AI models such as ChatGPT in place, chatbots are presenting improvements in language recognition abilities. According to the Market Research Report, the global Generative AI market is poised for exponential growth, expected to surge from USD 8.65 billion in 2022 to USD 188.62 billion by 2032, with a staggering CAGR of 36.10% during the forecast period of 2023-2032. The dominance of the North American region in the market in 2022 underscores the widespread adoption and recognition of the potential of Generative AI.

Why Is RAG Important?

Every industry hopes to evolve AI implementation, such as Generative AI, which can exploit big data to bring meaningful insights and solutions or provide more customization and automation to capitalize on AI potential. However, Generative AI leveraging neural network architectures and large language models (LLMs) helps businesses to improve with the limitation of producing content or analysis that may be factually wrong given the scope of data fed to the developed model, also known as “hallucinations” or providing outdated information.

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