Prompts can be natural language questions, code snippets, or commands, but for the LMM to do its job accurately, the prompts have to be on point.Īnd that necessity has given rise to a new skill: prompt engineering. A question or request sent to a chatbot is called a prompt, in that the user is prompting a response. Most LLMs can be accessed through an application programming interface (API) that allows the user to create parameters or adjustments to how the LLM responds. An LLM tuned to the financial services industry can summarize earnings calls, create meeting transcripts, and perform fraud analysis to protect consumers.Īcross various industries, ensuring a high degree of response accuracy can be paramount. LLMs for the healthcare industry, for instance, might need to process and interpret electronic health records (EHRs), suggest treatments, or create a patient healthcare summary based on physician notes or voice recordings. Many industries, however, require more customized LLM algorithms, those that understand their jargon and produce content specific to their users. And while LLM-based chatbots have produced their share of errors, pretrained LLMs work relatively well at feeding mostly accurate and compelling content that, at the very least, can be used as a jumping off point. Most LLMs, such as OpenAI’s GPT-4, are pretrained as next word or content prediction engines - that is how most businesses use them, “ out of the box,” as it were. Salesforce also announced plans to release a GPT-based chatbot for use with its CRM platform. In this case, Salesforce's Einstein chatbot is enabled through the use of OpenAI's GPT-3.5 large language model.įor example, the new Microsoft 365 Copilot can be used in Word to create a first draft of a document, potentially saving hours of time writing, sourcing, and editing. An example of generative AI creating software code through a user prompt.
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