New research and explorations by Dell Technologies and Intel have shed light on the challenges and opportunities in deploying generative AI and large language models (LLMs) in enterprise settings. While many organizations have been cautious about using commercially available models due to data access and security risks, Dell Technologies and Intel have taken a proactive approach in understanding their customers’ needs.

Through extensive investigation, six valuable lessons have been learned that can significantly improve outcomes and reduce risks:

Lesson 1: Avoid starting from scratch when training LLM models. Instead, opt for fine-tuning on pre-trained models or use prompting engineering techniques to optimize their output.

Lesson 2: LLMs go beyond text generation and have immense potential for natural language processing (NLP) tasks such as user intent identification, classification, semantic search, sentiment analysis, and even text-to-image generation.

Lesson 3: While open-source LLMs have rapidly evolved and expanded, they still have limitations. A workaround is to build systems with multiple LLMs that work in tandem while preventing excessive reliance on each other.

Lesson 4: Pay equal attention to input data sources as they directly impact the quality of LLM outcomes. Leveraging structures like knowledge graphs and advanced parsing techniques can greatly enhance results.

Lesson 5: Cost is a crucial consideration in deploying LLMs. While training and running inference can be expensive, there are ways to optimize by using less expensive cloud instances and on-premises data centers.

Lesson 6: Embrace the uniqueness of your specific problem and tailor LLM models accordingly. Invest in user interfaces that facilitate rich input information, guide users, and evaluate meaningful and relevant outputs.

By incorporating these lessons, organizations can harness the transformative power of generative AI and LLMs to address their specific needs effectively. Dell Technologies and Intel have paved the way towards a future where the potential of these technologies can be fully realized without compromising data access, security, or privacy concerns. The possibilities are vast and exciting as enterprises embark on this journey of exploration and innovation.