Welcome to your Prompt Engineering
Prompt Engineering
Which AI model made prompt engineering popular?
ChatGPT
PyTorch
Google BERT
TensorFlow
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Prompt Engineering
What is a “prompt”?
A database
A programming function
A type of chatbot
A command or instruction given to an AI model
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Prompt Engineering
Which of the following is NOT a prompt
Compiler prompt
Image prompt
Instruction prompt
Zero-shot prompt
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Prompt Engineering
What is “zero-shot prompting”?
Using multiple examples
Prompting without examples
Training data prompts
Prompting with one example
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Prompt Engineering
What does “few-shot prompting” mean?
Providing a few examples to guide the model
Providing one example
Providing large training data
Giving random inputs
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Prompt Engineering
Which of these tools is commonly used for generating images from prompts?
GitHub
TensorFlow
Notepad++
DALL·E
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Prompt Engineering
Which of these is a best practice in prompt writing?
Use clear, specific instructions
Avoid examples
Be vague and short
Write in code format only
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Prompt Engineering
What is “Chain of Thought” prompting?
A series of reasoning steps given in a prompt
Debugging technique
Neural network chain
A type of chatbot conversation
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Prompt Engineering
What is the benefit of providing context in prompts?
Makes the model run faster
Reduces response time
Helps the model understand intent and produce accurate output
Increases token limit
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Prompt Engineering
What is a “system prompt” in ChatGPT?
Debugging log
A user’s direct question
Output format
A background instruction defining model behavior
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Prompt Engineering
What is tokenization in LLMs?
Combining text blocks
Encrypting the data
Breaking text into smaller pieces for processing
Deleting unnecessary data
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Prompt Engineering
Which OpenAI model is known for image understanding and generation?
Whisper
DALL·E
Codex
Embeddings
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Prompt Engineering
Which prompt gives better results?
Code snippets only
Ambiguous and short
Clear, detailed, and goal-oriented
Random keywords
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Prompt Engineering
What does “temperature” control in AI generation?
File size
Model memory
Creativity or randomness in responses
Speed of processing
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Prompt Engineering
Which of the following prompt styles improves factual accuracy?
Open-ended vague prompting
Random phrasing
Emotional prompting
Role-based prompting (“You are a data scientist…”)
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Prompt Engineering
What is the ideal way to refine a prompt?
Experiment and iterate based on model response
Change models
Use longer sentences only
Avoid testing
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Prompt Engineering
What is prompt chaining?
Using multiple related prompts to build complex tasks
Combining two LLMs
Creating multiple accounts
Debugging a model
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Prompt Engineering
What is “context window” in LLMs?
The model’s dataset
The number of tokens the model can remember in one interaction
The time the model runs
The length of the output
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Prompt Engineering
What is a “negative prompt” (in image generation)?
A prompt with negative numbers
A prompt that deletes images
Prompt that asks model to avoid certain features
A random prompt
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Prompt Engineering
Which of the following improves model reliability in prompt engineering?
Using fewer words
Copying prompts from others
Using role + task + context + format structure
Using random emojis
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Prompt Engineering
What does “few-shot learning” enable LLMs to do?
Learn new tasks from few examples
Forget old data
Generate images only
Train on large datasets
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Prompt Engineering
Which of these helps prevent hallucinations in AI output?
Provide source references or constraints in the prompt
Use shorter prompts
Use random data
Use higher temperature
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Prompt Engineering
What is the main purpose of prompt engineering in generative AI?
To run hardware optimization
To train the model
To build datasets
To improve the quality and relevance of AI outputs
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Prompt Engineering
What does “multi-modal prompting” refer to?
Using text + image + audio inputs together
Using multiple CPUs
Running two models
Using multi-language prompts
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Prompt Engineering
What is the main advantage of iterative prompting?
Using more tokens
Refining outputs through step-by-step improvements
Running model faster
Avoiding context
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