WGU D685 - Practical Applications of Prompt
- fundamental limitations of AI The limitations of AI can be categorized into three main areas:
- practical limitations and challenges
- societal concerns and implications.
- dependence on training data Fundamental limitations of AI include:
- limited common sense
- lack of emotional sense.
- perpetuating bias Practical limitations of AI include:
- lack of ethics
- understanding nuances of language and humans.
- data privacy Societal concerns on AI include:
- 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
, WGU D685 - Practical Applications of Prompt
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
, WGU D685 - Practical Applications of Prompt
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
- fundamental limitations of AI The limitations of AI can be categorized into three main areas:
- practical limitations and challenges
- societal concerns and implications.
- dependence on training data Fundamental limitations of AI include:
- limited common sense
- lack of emotional sense.
- perpetuating bias Practical limitations of AI include:
- lack of ethics
- understanding nuances of language and humans.
- data privacy Societal concerns on AI include:
- 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
, WGU D685 - Practical Applications of Prompt
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
, WGU D685 - Practical Applications of Prompt
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
