Small language models (SLMs) are emerging as a more efficient and accessible alternative to large language models (LLMs). Despite having less processing power, some SLMs achieve comparable performance to LLMs in specific tasks, making them attractive for businesses with limited resources and specific needs. READ ARTICLE
Prompt engineering and fine-tuning are both effective techniques for improving the performance of generative AI models, but they serve different purposes and have different resource requirements.
Prompt engineering is the process of carefully crafting the prompts and inputs to AI models like GPT-3.5 in order to get the desired high-quality outputs. It involves tweaking parameters like wording, context, temperature, max tokens, and task decomposition. READ ARTICLE
The key takeaway from the article is that combining large language models (LLMs) like GPT-3 with conventional optimization algorithms through function calling provides an effective way to build conversational agents that can provide optimal solutions for managing flight disruptions. This improves transparency and explainability of recommendations. READ ARTICLE