Evaluating the Effectiveness of Prompt Engineering in Salesforce Prompt Studio
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I3P111Keywords:
Prompt Engineering, Salesforce Prompt Studio, Generative AI, Large Language Models (Llms), AI Optimization, Natural Language Processing (NLP), Prompt Design Strategies, AI Productivity Tools, Context-Aware Responses, Workflow AutomationAbstract
The appearance of Large Language Models (LLMs) changed the environment of enterprise applications and allowed natural language to be used to automate processes, communicate to the customer, and analyze the data. In this ecosystem, timely engineering has become one of the most efficient definitions of the output quality, particularly in those platforms incorporating instructions written by users to produce text, such as Salesforce Prompt Studio. The present paper explores the effect of various prompt design approaches, i.e. few-shot prompting, template-based inputs, and contextual primes, on the performance and reliability of AI output on Salesforce Prompt Studio. Several use cases are considered in the study, such as customer service automation, lead scoring and email generation. Creating and comparing the variants of prompts based on such metrics as the factual accuracy, relevance, alignment with the tone, the satisfaction of the users, the paper demonstrates an assessable relationship between the prompt design and the quality of the outcome. The results indicate outstanding practices in timely development and provide suggestions on enterprise-scale optimal prompt engineering. The study is relevant to the emerging market of applied LLMs and supplies prompt optimization into the context of realistic business problems and with the evidence of improved productivity and consistency outcomes in CRM settings
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