WGU Updated Practical-Applications-of-Prompt Exam Questions and Answers by uthman

Transparencyin AI ethics refers to the degree to which an AI system’s internal logic, data sources, and decision-making processes are visible and understandable to humans. It is the direct antidote to the "Black Box" problem. When an AI system provides a recommendation, the principle of transparency ensures that stakeholders (such as regulators, developers, and end-users) can understand the "why" behind the output. This is often achieved through "Explainable AI" (XAI) techniques.

In practical prompt engineering, transparency is optimized by instructing the model to provide its reasoning. For example, using "Chain of Thought" prompting forces the AI to list the steps it took to arrive at a conclusion. This makes the interaction transparent because the user can see if the AI relied on faulty logic or biased data. Transparency builds trust; if a user understands how an AI reached a conclusion, they are more likely to adopt the technology. Furthermore, transparency is a prerequisite for other ethical principles like Fairness and Accountability, as you cannot fix a bias or hold a system accountable if you cannot see how it functions internally.

This scenario describes the application ofConstraintswithin a prompt. Constraints are the specific boundaries, limitations, or "rules" that the AI must follow when generating a response. In this instance, the constraint is the character limit. Because the chatbot operates via text (likely SMS or a narrow chat window), long-form responses would be technically or practically problematic. By setting a character limit, the prompt engineer is forcing the AI to prioritize brevity and essential information.

Constraints are vital in professional AI applications to ensure that the output is "fit for purpose." They go beyond the general "Output format" (which might just specify "a list" or "an email") by providing hard logical or physical parameters. Other common constraints include "do not use jargon," "avoid mentioning competitors," or "write at a fifth-grade reading level." In the development of customer service bots, constraints help maintain a consistent user experience and ensure that the AI's behavior aligns with the technical requirements of the platform. Managing constraints effectively is one of the most important skills in prompt engineering, as it prevents the AI from becoming too wordy (verbosity) or wandering off-topic.

The most effective way to optimize AI for clarity and comprehension is toinclude reading-level limitations. While "summarizing" (Option B) shortens the text, it doesn't necessarily make the remaining language simpler. However, specifying a "tenth-grade reading level" (or "Explain it like I'm five") provides the AI with a very specific linguistic constraint. It forces the model to swap complex jargon for common synonyms, use shorter sentence structures, and avoid passive voice.

This technique is a form ofOutput Constraint. Reading levels are well-defined metrics that AI models can emulate because they have been trained on vast amounts of graded educational material. By setting this boundary, the user ensures the output is accessible to a broader audience without losing the core technical meaning. In practical professional settings—such as translating a medical white paper for a patient or a legal contract for a small business owner—this type of prompting is essential. It transforms dense, "impenetrable" text into actionable information, demonstrating how specific constraints can be used to reformat and simplify complex data sets effectively.

Generative AI models, specifically Large Language Models (LLMs), are highly effective atIdentifying inaccuracieswithin a dataset during the data cleaning phase. When provided with a dataset and a prompt to "check for consistency" or "identify anomalies," the AI can cross-reference the data points against its internal knowledge base or the logical rules established in the prompt. For example, if a list of "US States" includes "London," the AI can flag this as an inaccuracy.

This capability extends to identifying spelling errors, formatting inconsistencies (e.g., dates written in multiple formats), and logical contradictions. While AI can help in identifying bias (Option D), that is usually considered a higher-level "auditing" task rather than a standard "cleaning" task. Identifying inaccuracies is a foundational step in the data pipeline; by cleaning the data first, the user ensures that any subsequent analysis or "conclusion drawing" (Option C) is based on high-quality, reliable information. In prompt engineering, this is often performed using the "Self-Correction" or "Reviewer" pattern, where one prompt generates data and a second prompt is used specifically to identify and fix any factual or structural inaccuracies within that output.