To improve the quality and relevance of responses generated by a generative AI solution using GPT-3.5, the following three actions are emphasized in the Microsoft Learn: Azure OpenAI Service best practices and AI-
900/AI-102 training materials:
* (A) Add grounding data to prompts: Grounding ensures that the model's output is based on factual, domain-specific information. By adding context or external knowledge sources, responses become more accurate and aligned with the organization's data rather than relying on the model's general training corpus.
* (B) Provide additional examples to prompts: Also known as few-shot prompting, this method demonstrates desired response patterns by including examples in the prompt. This significantly improves output quality, consistency, and adherence to desired formats.
* (E) Modify system messages: In Azure OpenAI chat completions, system messages define the model's behavior, style, and tone. Adjusting system messages allows fine-tuning of the model's response quality, ensuring it follows context or persona guidelines.
The remaining options are incorrect:
* (C) Modify tokenization is a low-level text-processing technique not used to improve model response quality directly.
* (D) Add training data to prompts is not possible at runtime since GPT-3.5 models are pre-trained; only prompt engineering can influence output behavior.
Therefore, based on Azure OpenAI and AI-900 guidance, the three best ways to enhance generative