When a major vendor such as Databricks

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Second, you need to build an AI-educated workforce. Research points to the fact that techniques like advanced prompt engineering can prove useful in identifying and mitigating hallucinations. Other methods, such as fine-tuning, have been shown to dramatically improve LLM accuracy, even to the point of outperforming larger, more advanced general purpose models. However, employees can only deploy these tactics if they’re empowered with the latest training and education to do so. And let’s be honest: most employees aren’t. We are just over the one-year mark since the launch of ChatGPT on November 30, 2022!

or Snowflake releases new capabilities, organizations flock to webinars, conferences, and workshops to ensure they can take advantage of the latest features. Generative AI should be no different. Create a culture in 2024 where educating your team on AI best practices is your default; for example, by providing stipends for AI-specific L&D programs or bringing in an outside training consultant, such as the work we’ve done at data.world with Rachel Woods, who serves on our Advisory Board and founded and leads The AI Exchange. We also promoted Brandon Gadoci, our first data.world employee outside of me and my co-founders, to be our VP of AI Operations. The staggering lift we’ve already had in our internal productivity is nothing short of inspirational (I wrote about it in this three-part series.) Brandon just reported yesterday that we’ve seen an astounding 25% increase in our team’s productivity through the use of our internal AI tools across all job roles in 2023! Adopting this type of culture will go a long way toward ensuring your organization is equipped to understand, recognize, and mitigate the threat of hallucinations.

Third, you need to stay on top of the burgeoning AI ecosystem. As with any new paradigm-shifting tech, AI is surrounded by a proliferation of emerging practices, software, and processes belgium whatsapp number data to minimize risk and maximize value. As transformative as LLMs may become, the wonderful truth is that we’re just at the start of the long arc of AI’s evolution.

Technologies once foreign to your organization may become critical. The aforementioned benchmark we released saw LLMs backed by a knowledge graph – a decades-old architecture for contextualizing data in three dimensions (mapping and relating data much like a human brain works) – can improve accuracy by 300%! Likewise, technologies like vector databases and retrieval augmented generation (RAG) have also risen to prominence given their ability to help address the hallucination problem with LLMs. Long-term, the ambitions of AI extend far beyond the APIs of the major LLM providers available today, so remain curious and nimble in your enterprise AI investments.

Like any new technology, generative AI solutions are not perfect, and their tendency to hallucinate poses a very real threat to their current viability for widespread enterprise deployment. However, these hallucinations shouldn’t stop organizations from experimenting and integrating these models into their workflows. Quite the opposite, in fact, as so eloquently stated by AI pioneer and Wharton entrepreneurship professor Ethan Mollick: “...understanding comes from experimentation.” Rather, the risk hallucinations impose should act as a forcing function for enterprise decision-makers to recognize what’s at stake, take steps to mitigate that risk accordingly, and reap the early benefits of LLMs in the process. 2024 is the year that your enterprise should take the leap.