How To Reduce AI Hallucinations

Sponsored Post Accurate data is fundamental to the success of generative AI (GenAI) - and so is Retrieval-Augmented Generation (RAG).

RAG can enhance the accuracy of GenAI workloads by processing large amounts of information pulled from external vector databases which the learning models at their core would not usually access. In doing so, it not only fine-tunes underling large (LLMs) and small language models (SLMs) by swapping in fresh data but also reduces the need to continually retrain them.

That can provide a significant boost for GenAI applications which rely on constantly changing datasets, such as healthcare or finance for example, or any scenario which uses virtual assistants, chatbots or knowledge engines. But in order to retrieve accurate, up to date responses rather than AI hallucinations from those dynamic datasets, RAG inferencing also needs a fast, scalable compute architecture.

That's something that not every enterprise has in house, and often these organizations lack the budget to implement. You can watch this video interview to hear The Register's Tim Philips talk to Infinidat CMO Eric Herzog about the infrastructure cost and complexity barriers which have stopped many organizations from building their LLMs in-house and how to get around them.

Introduced last November, Infinidat's RAG workflow deployment architecture is designed specifically to address those challenges, by working in tandem with existing InfiniBox and InfiniBox SSA enterprise storage systems to optimize the output of AI models without the need to invest in specialized equipment. It can also be configured to harness RAG in multi-cloud environments, using Infinidat's InfuzeOS Cloud Edition, and comes with embedded support for common cloud-hosted vector database engines such as Oracle, Postgres, MongoDB and DataStax enterprise. Infinidat's RAG solution will also work on non-Infinidat base storage systems with NFS-based data that can be integrated into the overall RAG configuration.

You can read more about Infinidat's RAG workflow deployment architecture here, alongside details on potential use cases which range from AI Ops, business intelligence, chatbots and educational tools to healthcare information, industrial automation, legal research and support.

Sponsored by Infinidat.