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Generative AI, ChatGPT and Large Language Models: A 101

Acuvate

The introduction of ChatGPT and other cutting-edge AI-led data analytics and visualization tools has sparked a lot of buzz in the tech world. The secret to these models’ success is Generative AI, which creates text that sounds remarkably human, enabling business users with data analytics outcomes that feels far more natural.

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How to Use Generative AI and LLMs to Improve Search

TechEmpower Innovation

Artificial Intelligence (AI), and particularly Large Language Models (LLMs), have significantly transformed the search engine as we’ve known it. With Generative AI and LLMs, new avenues for improving operational efficiency and user satisfaction are emerging every day.

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Generative AI – The End of Empty Textboxes

TechEmpower Innovation

On a different project, we’d just used a Large Language Model (LLM) - in this case OpenAI’s GPT - to provide users with pre-filled text boxes, with content based on choices they’d previously made. That blurb, and the following examples, were all generated from GPT in only a few seconds, at a cost of less than one penny.

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Innovation on Steroids: Next Generation AI-Powered Phases and Gates

Leapfrogging

AI technologies bring a new dimension of analytical capabilities and insights that were previously unattainable. By harnessing the power of AI, organizations are able to process vast amounts of data, identify patterns, and make more informed decisions at every phase of the innovation process.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.

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Is Your Company’s Data Ready for Generative AI?

Harvard Business Review

While CDOs and data leaders are excited about generative AI, they have much work to do to get ready for it. Despite excitement, companies have yet to see clear value from generative AI and need to do significant work to prepare their data.

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Managing the Risks of Generative AI

Harvard Business Review

Generative artificial intelligence (AI) has become widely popular, but its adoption by businesses comes with a degree of ethical risk. Organizations must prioritize the responsible use of generative AI by ensuring it is accurate, safe, honest, empowering, and sustainable.

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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.

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Generative AI Deep Dive: Advancing from Proof of Concept to Production

Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage

Executive leaders and board members are pushing their teams to adopt Generative AI to gain a competitive edge, save money, and otherwise take advantage of the promise of this new era of artificial intelligence.