D685 Practical Applications of Prompt - Set 1 - Part 1

Correct Answer: Zero-shot prompting

Zero-shot prompting asks the model to perform tasks without providing any examples, relying solely on its pre-trained knowledge for generating responses.
Option a is incorrect because few-shot prompting specifically requires providing limited examples to guide the model's response patterns and improve accuracy.
Option c is incorrect because chain-of-thought prompting focuses on encouraging step-by-step reasoning rather than operating without examples or training data.
Option d is incorrect because tree-of-thought prompting explores multiple reasoning paths but doesn't specifically address the absence of training examples.

Correct Answer: Selection bias

Selection bias occurs when training data is non-representative, causing the AI to favor certain groups that were overrepresented in its training dataset.
Option a is incorrect because measurement bias involves errors in how data is collected or recorded, not demographic favoritism in outputs.
Option c is incorrect because confirmation bias reinforces existing beliefs rather than causing systematic favoritism toward specific demographic groups.
Option d is incorrect because algorithmic bias stems from the algorithm's design itself, not from unbalanced representation in training data.

Correct Answer: Ensure data transparency

Data transparency is crucial for ethical AI implementation in employee analysis, ensuring individuals understand how their data is used and processed.
Option a is incorrect because maximizing automation speed doesn't address the critical privacy concerns inherent in analyzing employee performance data.
Option c is incorrect because prioritizing cost efficiency over ethical considerations could lead to inadequate privacy protections and potential harm.
Option d is incorrect because increasing processing volume without considering privacy implications could violate ethical principles and employee trust.

Correct Answer: Optical character recognition

OCR technology specifically digitizes physical documents by converting printed or handwritten text into machine-readable digital format for searching and analysis.
Option a is incorrect because voice recognition systems identify and authenticate speakers through audio input, not process written documents.
Option b is incorrect because speech synthesis generates human-like speech from text, which is the opposite of what's needed here.
Option d is incorrect because while computer vision analyzes visual information broadly, OCR specifically handles text extraction from documents.

Correct Answer: Persona role definition

Persona definition instructs the AI to adopt a specific role or perspective, directly controlling the professional tone and viewpoint throughout responses.
Option a is incorrect because output format specification determines the structure of responses, not the tone or perspective adopted.
Option b is incorrect because context provides background information but doesn't specifically establish the AI's role or professional tone.
Option d is incorrect because constraints set limitations and parameters but don't define the specific professional perspective to adopt.

Correct Answer: Self-consistency technique

Self-consistency technique involves running prompts multiple times to identify and ensure consistent output patterns, directly addressing variability issues.
Option a is incorrect because generated knowledge prompting helps models create helpful context before tasks, not ensure consistency across runs.
Option c is incorrect because least-to-most prompting gradually increases complexity but doesn't specifically address output consistency problems.
Option d is incorrect because cognitive verifier method involves asking clarifying questions for comprehension, not ensuring consistent outputs.

Correct Answer: Data preprocessing cleaning

Data preprocessing cleaning specifically involves removing duplicates, correcting errors, and preparing data for AI analysis through systematic data preparation.
Option b is incorrect because dynamic sorting criteria involves organizing data by multiple factors, not cleaning and error correction.
Option c is incorrect because real-time prioritization routing deals with directing data flow based on importance, not data cleaning.
Option d is incorrect because pattern identification clustering groups similar data together rather than removing duplicates and correcting errors.

Correct Answer: Human autonomy preservation

Human autonomy preservation ensures that humans maintain control over AI decisions, especially critical ones affecting health and wellbeing outcomes.
Option a is incorrect because system transparency relates to understanding AI decision-making processes, not maintaining human control over decisions.
Option c is incorrect because privacy protection focuses on safeguarding sensitive data rather than preserving human decision-making authority.
Option d is incorrect because misinformation prevention addresses false information generation, not the maintenance of human control.

Correct Answer: Supervised learning paradigm

Supervised learning uses labeled data with known outcomes, making it ideal when you have customer queries paired with correct responses.
Option a is incorrect because unsupervised learning finds patterns in unlabeled data without explicit guidance or known correct responses.
Option b is incorrect because reinforcement learning learns through environmental interaction via rewards and penalties, not labeled examples.
Option d is incorrect because deep learning is a technique using neural networks, not a paradigm for utilizing labeled training data.

Correct Answer: Midjourney creative platform

Midjourney specifically combines AI capabilities with human creativity for artistic refinement, meeting both automated generation and creative control needs.
Option a is incorrect because DALL-E focuses primarily on text-to-image generation without emphasizing the human creative refinement aspect.
Option c is incorrect because InVideo merges AI visuals with video production rather than focusing on image generation with artistic refinement.
Option d is incorrect because OCR processes text from images rather than generating images from text descriptions.

Correct Answer: Hallucination generation issue

Hallucination in AI refers to generating false or inaccurate information that appears plausible, representing a key misinformation risk.
Option a is incorrect because selection bias involves non-representative training data, not the generation of factually incorrect information.
Option c is incorrect because sampling bias relates to disproportionate group representation rather than creating false but plausible content.
Option d is incorrect because measurement bias involves errors in data collection and recording, not generating misleading information.

Correct Answer: Document summarization tool

Document summarization tools specifically extract key insights from lengthy texts, providing quick understanding of complex legal documents efficiently.
Option a is incorrect because speech synthesis generates human-like speech from text rather than extracting insights from documents.
Option c is incorrect because voice recognition identifies speakers and processes audio, not written legal contracts and documents.
Option d is incorrect because computer vision analyzes visual information rather than extracting textual insights from legal documents.

Correct Answer: Explain the process step-by-step

Chain-of-thought prompting specifically encourages step-by-step reasoning, making this instruction directly activate the technique for complex problem-solving.
Option a is incorrect because providing multiple examples relates to few-shot prompting rather than chain-of-thought reasoning methodology.
Option c is incorrect because generating helpful context beforehand describes generated knowledge prompting, not chain-of-thought technique.
Option d is incorrect because asking clarifying questions initially relates to cognitive verifier method rather than step-by-step reasoning.

Correct Answer: Collecting diverse training data

Collecting diverse and representative training data directly addresses bias by ensuring all groups are fairly represented in model training.
Option a is incorrect because increasing processing speed doesn't address the underlying bias in how groups are represented.
Option c is incorrect because reducing operational costs has no impact on correcting biased representations in AI outputs.
Option d is incorrect because expanding storage capacity doesn't solve the fundamental problem of biased training data representation.

Correct Answer: Reinforcement learning paradigm

Reinforcement learning specifically learns through environmental interaction via rewards and penalties, perfect for trial-and-error strategy optimization.
Option a is incorrect because supervised learning requires labeled training data rather than learning through environmental interaction and feedback.
Option b is incorrect because unsupervised learning finds patterns in data without the trial-and-error interaction component described.
Option d is incorrect because transfer learning applies knowledge from one domain to another, not trial-and-error environmental learning.

Correct Answer: Overly broad prompt scope

Overly broad prompts directly cause unfocused responses because they lack the specificity needed to guide AI toward targeted outputs.
Option a is incorrect because missing context typically causes misunderstood queries rather than specifically unfocused and broad responses.
Option b is incorrect because unclear instructions result in irrelevant outputs rather than responses that are specifically too broad.
Option d is incorrect because incorrect output specification affects response structure and format, not the focus and scope of content.

Correct Answer: Physical and psychological safety

Safety considerations are paramount when designing for vulnerable populations like elderly users, ensuring no physical or psychological harm occurs.
Option a is incorrect because automation efficiency shouldn't override safety considerations when designing for potentially vulnerable user groups.
Option b is incorrect because cost reduction should never take precedence over ensuring safe and appropriate interactions for elderly users.
Option d is incorrect because processing speed optimization doesn't address the critical safety needs of vulnerable user populations.

Correct Answer: Tree-of-thought technique

Tree-of-thought technique specifically explores multiple reasoning paths, allowing the AI to consider various approaches before selecting solutions.
Option a is incorrect because zero-shot prompting performs tasks without examples rather than exploring multiple reasoning paths systematically.
Option c is incorrect because few-shot prompting provides limited examples for guidance, not exploration of multiple reasoning pathways.
Option d is incorrect because self-consistency checking ensures consistent outputs rather than exploring different reasoning paths deliberately.

Correct Answer: Real-time prioritization routing

Real-time prioritization and routing specifically handles categorizing and directing data like support tickets to appropriate departments instantly.
Option a is incorrect because outlier detection identifies unusual data points rather than categorizing tickets into departmental queues.
Option c is incorrect because missing value inference fills gaps in data rather than routing tickets to specific departments.
Option d is incorrect because contextual error correction fixes data mistakes rather than categorizing and routing support tickets.

Correct Answer: Zero-shot prompting approach

Zero-shot prompting is ideal when no training examples exist, allowing the model to generate recommendations using only its pre-trained knowledge.
Option a is incorrect because chain-of-thought focuses on step-by-step reasoning rather than working without available training examples.
Option b is incorrect because few-shot prompting specifically requires having limited examples available, which the institution lacks.
Option d is incorrect because generated knowledge prompting creates context for tasks but doesn't specifically address the absence of examples.