WGU D685 - Practical Applications of Prompt OA AND PA | FREQUENTLY TESTED QUESTIONS WITH CORRECT ANSWERS | BRAND NEW!

The limitations of AI can be categorized into three main areas:

- fundamental limitations of AI

- practical limitations and challenges

- societal concerns and implications.

Fundamental limitations of AI include:

- dependence on training data

- limited common sense

- lack of emotional sense.

Practical limitations of AI include:

- perpetuating bias

- lack of ethics

- understanding nuances of language and humans.

Societal concerns on AI include:

- data privacy

- safety

- security concerns

artificial intelligence (AI)

the study of creating machines and computer systems capable of performing tasks that typically require human intelligence

narrow AI

artificial intelligence that is designed and trained for a specific task or narrow set of tasks

general AI

a hypothetical future AI system that would possess human-level intelligence

algorithms

defined methods or processes employed to train models, generate predictions, and execute tasks using data

machine learning

a branch of AI that enables computers to improve their performance through experience without needing explicit programming

AI model

a computer program designed to make predictions or decisions based on input data

supervised learning

a technique where a model is trained using data that includes labeled examples, such as images with tagged objects or text with marked entities

unsupervised learning

a type of machine learning where the model is trained on unlabeled data without explicit guidance or supervision

reinforcement learning

a type of machine learning wherein an AI agent learns through interactions with an environment, garnering rewards or penalties contingent upon its actions

neural networks

computational models inspired by the structure and function of the human brain's neural networks that learn from data called training to recognize patterns, make predictions, and perform tasks such as classification, regression, and pattern recognition

deep learning

a powerful subset of machine learning that uses artificial neural networks to learn from large amounts of data

generative AI

AI systems that can create new content

large language models (LLMs)

a type of machine learning model that is trained on massive amounts of text data to understand and generate human-like language

natural language processing (NLP)

the field of AI concentrated on enabling computers to understand and engage with human language, mirroring the intricacies of human communication

chatbots

AI programs designed to engage in natural conversations with people, providing information, answering questions, and even offering emotional support

computer vision

a field of artificial intelligence that enables computers to interpret and analyze visual information from the real world, such as images and videos

robotics

the field of AI that focuses on designing, constructing, and operating robots

statistical analysis

a technique employed in AI that involves collecting, organizing, examining, and interpreting data to identify patterns and make predictions

AI tools

software programs designed to assist users in performing AI-related tasks

This is an example of Few-Shot Prompting

Match the example prompts with the technique:

"Write a dialogue between two friends planning a trip."

structured data

data that is organized in a well-defined format and is typically stored in databases or spreadsheets

unstructured data

includes text documents, images, videos, audio recordings, social media posts, and other types of data that do not fit neatly into a structured format

byte

a unit of digital information

gigabyte (GB)

a unit of digital information equal to approximately one billion bytes

terabyte (TB)

a unit of digital information equal to approximately one trillion bytes

hallucinations

an incorrect, misleading, nonsensical, or entirely fabricated output generated by an AI model

adversarial attack

deliberate attempts to deceive or manipulate AI systems by introducing carefully crafted perturbations to the input data

AI drift

the phenomenon where the performance of an AI model deteriorates over time as the underlying data distribution changes

generative adversarial networks (GANs)

a type of deep learning model that uses an adversarial training process to create new data

prompt

a user query, command, or input in an AI interface

interface

the means by which users interact with and provide input to AI models to generate desired outputs, such as text, images, or other forms of content

specificity

the degree to which prompts are tailored to the individual user's needs, preferences, and current context

temperature

a parameter in the LLM that influences the level of randomness or creativity in generated outputs

false positive

occurs when the model incorrectly generates outputs that are not relevant or accurate to the input or task

true negative

refers to instances where the model correctly identifies inputs or conditions as not meeting certain criteria or expectations

structured query language (SQL)

a specialized programming language used for managing and manipulating relational databases, facilitating tasks such as data retrieval, insertion, updating, and deletion, while also enabling database administration and schema definition.

persona

refers to a fictional character or user profile created to represent a specific demographic, behavior pattern, or set of characteristics

input content

the information or data provided to a system, model, or application as input for processing or analysis

output format

the structure, layout, and presentation style of the results or outputs generated by a system, model, or process

additional information

supplementary data or details provided alongside the main content to provide context, clarification, or background information

constraints

limitations, conditions, or rules that restrict the behavior, actions, or design choices of a system, model, or process

verbosity

the amount of detail or brevity in the prompt's language, affecting the extent of information communicated to the AI chatbot

tone

the emotional or expressive quality conveyed in a piece of text or communication

factual responses

statements or answers that provide accurate, objective information based on verifiable facts or evidence

summarizing text

the process of condensing a longer piece of text into a shorter, more concise version while retaining the key information and main ideas

summarizing code

involves condensing a software program or codebase into a shorter, more digestible form while preserving its functionality and logic

text extraction

the process of automatically identifying and extracting specific pieces of text or information from a larger document, dataset, or source

named entity recognition (NER)

an NLP task that involves identifying and classifying named entities (such as names of persons, organizations, locations, dates, and other proper nouns) within a piece of text

part-of-speech (POS)

an NLP task that involves assigning grammatical categories or tags to each word in a sentence based on its syntactic function and role within the sentence

text classification

an NLP task that involves categorizing text documents or instances into predefined classes or categories based on their content, themes, or characteristics

context

the surrounding circumstances, conditions, or information that influence the interpretation, meaning, or significance of something

user research

the systematic study of users' needs, behaviors, and preferences through various qualitative and quantitative methods to inform the design and development of products or systems

clarity

the quality of being easily understood, free from ambiguity or confusion, in the communication of information or instructions

consistency

the quality of maintaining uniformity, coherence, or stability in behavior, tone, or style across different interactions or contexts, fostering trust and familiarity with users

adaptability

the ability of artificial intelligence systems to adjust their responses, behaviors, or functionalities based on changing contexts, user preferences, or environmental conditions

usability

the extent to which a product or system can be used effectively, efficiently, and satisfactorily by users to achieve their goals

This is an example of Cognitive Verifier Pattern

Match the example prompts with the technique:

"Given a passage on quantum physics, explain the concept of superposition and its implications for quantum computing."

precision

the quality of AI responses that accurately address the specific requirements or queries outlined in the prompt, minimizing errors or irrelevant information

relevance

the degree to which AI responses align with the user's needs, preferences, or queries, ensuring that the information provided is useful and applicable

user experience

the overall quality of interactions between users and AI systems, encompassing factors such as ease of use, satisfaction, and effectiveness in meeting user needs

detailed prompts

clear and specific instructions provided to AI systems to guide their responses, often including context, specific queries, or instructions

ambiguity

lack of clarity or specificity in prompts or queries, leading to potential confusion or multiple interpretations by AI systems.

contextual understanding

AI systems' ability to comprehend the background, nuances, and specific requirements of user queries, enabling them to generate contextually appropriate responses

training and learning

the process through which AI systems acquire knowledge, refine their algorithms, and improve their performance over time based on input data, including detailed prompts

user intent

the underlying purpose or goal behind user queries or prompts, which AI systems strive to understand and address accurately

trust and reliability

users' confidence in the accuracy, consistency, and usefulness of AI-generated responses, built through reliable and relevant interactions over time

various domains

different fields or industries where AI systems are applied, such as healthcare, customer service, education, and finance, each with unique requirements and challenges in AI interaction

industry-specific jargon

specialized language or technical terms used within specific fields or communities, often unfamiliar to those outside of that context

This is an example of Generated Knowledge Prompting

Match the example prompts with the technique:

"Explain the water cycle." The language model provides an explanation of the water cycle, drawing upon commonly understood scientific knowledge about the process.

few-shot

a prompt engineering technique that offers a limited number of examples or "shots" related to a specific task or topic to guide the language model's response generation

chain-of-thought (CoT)

a prompt engineering technique used to encourage large LLMs to explain their reasoning when responding to a prompt that goes beyond just providing an answer and reveals the intermediate steps the LLM takes to arrive at that answer.

tree-of-thought prompting (ToT)

a prompt engineering technique used to encourage LLMs to explore different possibilities and consider multiple reasoning paths before generating a response

self-consistency

a prompt engineering technique that asks a model the same prompt multiple times and takes the most consistent result as the final answer.

This is an example of Least-to-Most Prompting

Match the example prompts with the technique:

User starts with "Dinner ideas" and refines to "Healthy vegetarian dinner ideas with less than 30 minutes preparation time."

structured prompts

detailed and specific instructions provided to AI generators, typically following a format that includes the image type, main subject, background scene, and composition style

modifiers

descriptive keywords or parameters included in prompts to specify additional details or characteristics of the desired output, such as mood, lighting, viewpoint, or style

iterative prompting

the process of refining prompts through successive iterations by adding additional details, modifiers, or parameters to improve the quality or specificity of the generated images

advanced prompting technique

techniques such as language processing, decision-making, and problem-solving, which include few-shot, zero-shot, tree-of-thought (ToT), chain-of-thought (CoT), and self-consistency that use structured and sophisticated methods to guide machine learning models, improving their performance and accuracy in tasks

bias

the presence of unfair or prejudiced outcomes in AI systems resulting from inherent biases in the data, algorithms, or design process, leading to unequal treatment or inaccurate predictions for certain individuals or groups

ethics

the moral principles, values, and guidelines governing the development, deployment, and use of AI systems to ensure they align with ethical standards, respect human rights, and promote fairness, transparency, accountability, and societal well-being

responsible AI

the ethical and accountable development, deployment, and use of AI systems to ensure they operate in a manner that prioritizes fairness, transparency, accountability, safety, and societal well-being while mitigating potential risks and negative impacts on individuals and communities

misinformation

the dissemination of false or inaccurate information generated by AI and is typically unintentional and results from the limitations of the AI's training data and algorithms, contrasting with disinformation, which is deliberately deceptive

data accuracy

the correctness and precision of the information contained within a dataset and is crucial to ensure that AI systems make reliable and accurate predictions or decisions based on the data they are trained on

data integrity

encompasses data's completeness, consistency, and reliability throughout its life cycle and ensures that data remains unchanged and consistent, guarding against unauthorized access, tampering, or corruption, thereby preserving the trustworthiness of AI-driven insights and models

input methods

the various ways users can provide input to AI image generation tools, including textual descriptions, sketches, and random prompts, influence the type and style of images generated

output quality

the level of detail, realism, and artistic style of the images produced by AI image generation tools, impacting their suitability for different creative and professional applications

interactive creation

a feature of some AI tools that involves user interaction to refine and improve generated images

AI-powered editing

the use of AI to automate various aspects of the video editing process, such as scene detection, caption generation, and the selection of background music, to streamline the creation of professional-quality video content

customization

features and functionalities that allow users to adjust and refine the generated images, such as iterative feedback, parameter adjustments, and style variations, enhancing creative control and output relevance

tool creation

the process of designing and developing new software applications, artistic content, design prototypes, and other innovative products using the capabilities of generative AI

speech recognition

AI technology enables computers to transcribe spoken words into text, facilitating hands-free operation and natural language interfaces

voice recognition

the process of distinguishing and confirming the speaker's identity, facilitating secure authentication systems and tailored user interactions

optical character recognition (OCR)

the AI-driven technology that converts printed or handwritten text into machine-readable format, enabling automated data entry, document processing, and image text extraction

data

the raw information, often in large volumes, used to train, validate, and test machine learning models, enabling them to learn patterns, make predictions, and perform tasks

data cleaning

the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets to ensure the quality and reliability of the data used for analysis and machine learning

classification

the process of categorizing data into predefined classes or groups based on input features using a machine learning algorithm