Understanding and Maximizing Large Language Models (LLMs)

With the Age of AI fully upon us, savvy organizations are investing their time and resources into finding the best AI solutions and using them to maximize productivity. As we discussed in the previous entry to this series, there are different levels of AI, including machine learning, deep learning, and generative AI.

Generative AI can produce text, images, audio, and more. While some organizations may not have use for images like these, nearly any company may find use in a Large Language Model (LLM). LLMs are designed to create human text. It has a multitude of applications, including translations, summarization, chatbots and digital assistants, and much more.

Optimizing LLMs

While the promise that LLMs hold is vast, users cannot expect them to be completely optimized right out of the box. The AI experts at Titan Consulting have laid out two key techniques companies should adopt to ensure that LLMs deliver high-quality results – fine-tuning and prompt engineering.

Fine-Tuning – To fine-tune an LLM, users adjust the pre-trained tool with a more focused dataset. This teaches the LLM to better manage the task at hand. The LLM already has a baseline foundation of training laid down and the fine-tuning process puts the finishing touches on its education, so to speak, using specific data to deliver outputs that are more applicable for a specific business.

Prompt Engineering – In addition to fine-tuning the LLM itself, users should practice prompt engineering to generate optimized outputs. Certain questions or commands will deliver better results, so users should take the time to learn how to generate more precise and ideal outputs. Many LLMs are trained to speak like a person would, so using clear and precise language is helpful. Setting ground rules and conveying background information can be helpful. Prompt engineering is not an exact science – some trial and error is to be expected.

What This Means for SAPinsiders

Trust the process and keep going. Getting the hang of LLMs may take a bit of time but should end up being worth the investment. Practicing how to get the best results out of an LLM through prompt engineering can save lots of time in the long run. Users who want to learn more about AI implementation and optimization can rely on experienced partners like Titan Consulting to learn more about best practices.

Precision is key. When utilizing LLMs, users should be precise – both in the questions they ask and the datasets they use for training. AI in general and LLMs specifically are only as powerful as the training and input they receive.

Explore use cases for LLMs. Large language models have an expansive set of potential uses that nearly any business could benefit from. Those companies that have no AI experience should see how other businesses have used LLMs to see if those use cases can make their business run more smoothly.