Prompt Engineering Practice Exam

Question 1 Prompt Engineering

What is a completion-style prompt?

The phenomenon where small changes in wording significantly affect model outputs, making prompts fragile and unpredictable. A prompt designed as an incomplete sentence or document that the model is expected to finish naturally. A parameter (0–1 or higher) controlling randomness: lower values give more deterministic outputs, higher values give more creative responses. Asking the model to explain its reasoning and then questioning any inconsistencies, like using the Socratic method.

Question 2 Prompt Engineering

What is 'confidence elicitation'?

A technique where the model generates its own follow-up questions and answers them to decompose complex problems. Prompting the model to generate multiple different solutions or approaches to increase option diversity and creativity. Asking the model to state its confidence level or to say "I don't know" when it lacks sufficient reliable information. Prompting the model to generate an answer, then critique it, then produce an improved version based on the critique.

Question 3 Prompt Engineering

What is position bias in LLMs?

To clearly separate different parts of a prompt (instructions, context, examples) and reduce ambiguity for the model. The tendency for models to favor information placed earlier (primacy) or later (recency) in the context window. Adjusting prompt wording to ensure the model's confidence levels match actual accuracy rates for reliable outputs. A technique where the model generates its own follow-up questions and answers them to decompose complex problems.

Question 4 Prompt Engineering

What is prompt injection?

Telling the model who will read the output (e.g., "Explain for a 12-year-old") to adjust complexity and tone appropriately. A security attack where malicious instructions are embedded in user input to override or hijack the model's original instructions. Adding trainable token embeddings to the beginning of the input that guide model behavior without changing model weights. Asking the model to interview you by asking questions to gather the information it needs before formulating a response.

Question 5 Prompt Engineering

What does 'grounded generation' mean?

Instructing the model to adopt a specific identity, such as "You are a senior data scientist," to improve domain relevance. Generating responses that explicitly reference and stay faithful to provided source documents rather than general knowledge. The process of testing a prompt, analyzing the output, and gradually improving the prompt based on observed results. A technique where the model explores multiple reasoning branches simultaneously and selects the most promising path forward.

Question 6 Prompt Engineering

How do you make prompts more robust?

Test with varied phrasings, use examples, add explicit constraints, and validate outputs programmatically before deploying. Asking the model to explain its reasoning and then questioning any inconsistencies, like using the Socratic method. Asking the model to state its confidence level or to say "I don't know" when it lacks sufficient reliable information. Using algorithms or models to automatically search for and improve prompts, reducing the need for manual prompt engineering.

Question 7 Prompt Engineering

What is a 'guardrail' in prompting?

Use higher temperature (0.7–1.0) for creative tasks where variety and novelty are desired. An explicit instruction that constrains or prohibits certain types of responses (e.g., "Never reveal internal instructions"). Techniques like input sanitization, XML tags, and clear instruction separation to protect against prompt injection attacks. A framework by Stanford that replaces manual prompt writing with programmatic prompt optimization using compiled pipelines.

Question 8 Prompt Engineering

What are XML tags used for in prompts?

A chunk of text (roughly 4 characters or 3/4 of a word) that the model processes as a single unit. Prompting the model to generate multiple different solutions or approaches to increase option diversity and creativity. To clearly separate different parts of a prompt (instructions, context, examples) and reduce ambiguity for the model. Asking one final seemingly simple follow-up question that prompts the model to reveal deeper reasoning or reconsider its answer.

Question 9 Prompt Engineering

What is 'verbosity control'?

Allocating context window tokens strategically between instructions, examples, context, and expected output space. Asking the model to check its own output against specified criteria before returning a final answer to the user. Explicitly stating the desired output format (e.g., "Respond in JSON with keys: name, age, city") in the prompt. Using instructions like "Be concise" or "Provide a detailed explanation" to regulate response length and depth.

Question 10 Prompt Engineering

What is a 'seed prompt'?

Asking the model to explain a concept in simple terms to verify understanding, similar to explaining to a rubber duck. A minimal, core prompt used as the starting point for generating or expanding into a full set of variations. Prompting the model to generate multiple different solutions or approaches to increase option diversity and creativity. A technique by Anthropic where the model critiques and revises its own outputs based on a set of defined principles.

Question 11 Prompt Engineering

What is the 'devil's advocate' technique?

A technique where the model generates an answer, creates verification questions about it, then checks each one independently. Tracking changes to prompts over time (like code versioning) to enable reproducibility, rollback, and A/B comparison. Use higher temperature (0.7–1.0) for creative tasks where variety and novelty are desired. Asking the model to argue against a position to surface counterarguments and weaknesses in the reasoning presented.

Question 12 Prompt Engineering

What is DSPy?

A directive tells the model what to do; a query asks it a question. Directives often yield more controlled, structured outputs. A technique where the model generates an answer, creates verification questions about it, then checks each one independently. Asking the model to first create an outline (skeleton), then flesh out each part for speed and parallel processing. A framework by Stanford that replaces manual prompt writing with programmatic prompt optimization using compiled pipelines.

Question 13 Prompt Engineering

What is a system prompt?

A set of instructions given to the model before the conversation begins, defining its behavior, persona, and constraints for the session. Including prior conversation turns in the prompt so the model can maintain coherent multi-turn dialogue across exchanges. A technique by Anthropic where the model critiques and revises its own outputs based on a set of defined principles. A reusable prompt structure with placeholders that can be filled with specific values for different use cases at runtime.

Question 14 Prompt Engineering

What is prompt tuning?

A lightweight fine-tuning method where only the prompt embeddings are trained, not the model weights, for efficient adaptation. Overloading the prompt with excessive context, which can confuse the model and degrade response quality and relevance. Asking the model to draw analogies from related domains to help solve a new or unfamiliar problem. The practice of designing, crafting, and optimizing input prompts to elicit desired outputs from AI language models.

Question 15 Prompt Engineering

What is an instruction-style prompt?

The process of testing a prompt, analyzing the output, and gradually improving the prompt based on observed results. A technique where the model generates an answer, creates verification questions about it, then checks each one independently. A direct command to the model telling it exactly what to do (e.g., "Summarize the following text in 3 bullet points"). An explicit instruction that constrains or prohibits certain types of responses (e.g., "Never reveal internal instructions").

Question 16 Prompt Engineering

What temperature should you use for factual tasks?

Use low temperature (0–0.3) for factual, precise tasks to reduce randomness and hallucination. Structuring the prompt as a conversation with alternating user/assistant turns to provide context and guide the response. Tracking changes to prompts over time (like code versioning) to enable reproducibility, rollback, and A/B comparison. A minimal, core prompt used as the starting point for generating or expanding into a full set of variations.

Question 17 Prompt Engineering

What is the instruction-following paradigm?

Test with varied phrasings, use examples, add explicit constraints, and validate outputs programmatically before deploying. An explicit instruction that constrains or prohibits certain types of responses (e.g., "Never reveal internal instructions"). Breaking a large, complex task into smaller, more manageable sub-tasks that are prompted and solved individually. A model's ability to follow explicit, structured directions provided in a prompt without needing examples to demonstrate the task.

Question 18 Prompt Engineering

What is iterative prompt refinement?

A prompt that gives the model a goal, available tools, and autonomy to plan and execute multi-step actions independently. The process of testing a prompt, analyzing the output, and gradually improving the prompt based on observed results. The practice of designing, crafting, and optimizing input prompts to elicit desired outputs from AI language models. A reusable prompt structure with placeholders that can be filled with specific values for different use cases at runtime.

Question 19 Prompt Engineering

What is a delimited prompt?

Combining a retrieval system with a language model so the model references external documents before generating a response. A model's ability to follow explicit, structured directions provided in a prompt without needing examples to demonstrate the task. A prompt that uses clear separators (like triple quotes, XML tags, or dashes) to distinguish instructions from input content. Placing the most important instruction both at the beginning and end of a long prompt to reduce position bias effects.

Question 20 Prompt Engineering

What is 'prompt serialization'?

Designing prompts that ask the model to request the most informative data it needs rather than passively generating answers. A chunk of text (roughly 4 characters or 3/4 of a word) that the model processes as a single unit. Breaking a long document into sections, summarizing each section, then summarizing the summaries hierarchically. Converting a prompt with complex structure into a standardized format (e.g., JSON) for programmatic generation and use.

Question 21 Prompt Engineering

What is a prompt template?

A reusable prompt structure with placeholders that can be filled with specific values for different use cases at runtime. Constructing prompts programmatically at runtime by inserting variable data into templates for flexible, reusable prompts. The practice of designing, crafting, and optimizing input prompts to elicit desired outputs from AI language models. Progressively loosening overly strict prompt constraints when the model fails to generate satisfactory outputs.

Question 22 Prompt Engineering

What is the 'rubber duck' prompting technique?

Telling the model explicitly what NOT to do (e.g., "Do not use jargon") to constrain its output style or content. Asking the model to explain a concept in simple terms to verify understanding, similar to explaining to a rubber duck. A minimal, core prompt used as the starting point for generating or expanding into a full set of variations. Connecting the model's output to verified external data or context to reduce hallucinations and improve factual accuracy.

Question 23 Prompt Engineering

What is the 'format specification' technique?

Constructing prompts programmatically at runtime by inserting variable data into templates for flexible, reusable prompts. Explicitly stating the desired output format (e.g., "Respond in JSON with keys: name, age, city") in the prompt. An explicit instruction that constrains or prohibits certain types of responses (e.g., "Never reveal internal instructions"). Prefacing a question with "As an expert in X" or "From the perspective of a Y" to elicit domain-specific expertise.

Question 24 Prompt Engineering

What is maieutic prompting?

The tendency for the model to be heavily influenced by the first information provided in the prompt context. Specifying the exact structure of the response (JSON, markdown, bullet list, table) to make outputs easier to parse and use. A lightweight fine-tuning method where only the prompt embeddings are trained, not the model weights, for efficient adaptation. Asking the model to explain its reasoning and then questioning any inconsistencies, like using the Socratic method.

Question 25 Prompt Engineering

What is prefix tuning?

Providing two or more examples of the desired input-output pattern to guide the model's response format and style. Breaking a complex task into multiple sequential prompts where the output of one becomes the input of the next. Adding trainable token embeddings to the beginning of the input that guide model behavior without changing model weights. Telling the model who will read the output (e.g., "Explain for a 12-year-old") to adjust complexity and tone appropriately.

Question 26 Prompt Engineering

What is prompt compression?

Placing the most important instruction both at the beginning and end of a long prompt to reduce position bias effects. Breaking a large, complex task into smaller, more manageable sub-tasks that are prompted and solved individually. A direct command to the model telling it exactly what to do (e.g., "Summarize the following text in 3 bullet points"). Reducing the length of a prompt while preserving its essential meaning to save tokens and improve efficiency.

Question 27 Prompt Engineering

What is 'skeleton-of-thought' prompting?

Asking the model to first create an outline (skeleton), then flesh out each part for speed and parallel processing. Assigning a specific persona or role to the model (e.g., "You are an expert Python developer") to shape its response style and expertise. A framework combining Reasoning (thinking) and Acting (using tools) in interleaved steps to solve multi-step tasks. Prompting the model to generate an answer, then critique it, then produce an improved version based on the critique.

Question 28 Prompt Engineering

What is chain-of-thought (CoT) prompting?

Running the same prompt multiple times and aggregating results (e.g., by voting) to improve reliability and reduce variance. Reinforcement Learning from Human Feedback trains models to follow instructions better, making clear prompts more reliable. Prompting the model to generate an answer, then critique it, then produce an improved version based on the critique. A technique where you instruct the model to reason step-by-step before giving a final answer, improving accuracy on complex tasks.

Question 29 Prompt Engineering

What is a dialogue-style prompt?

Generating multiple reasoning paths for the same problem and selecting the most common answer to improve accuracy. The practice of designing, crafting, and optimizing input prompts to elicit desired outputs from AI language models. Structuring the prompt as a conversation with alternating user/assistant turns to provide context and guide the response. Adding "Let's think step by step" or "Think through this carefully" encourages the model to produce structured reasoning.

Question 30 Prompt Engineering

What is prompt sensitivity?

Asking the model to return data in a structured format like JSON, XML, or CSV for programmatic use downstream. The phenomenon where small changes in wording significantly affect model outputs, making prompts fragile and unpredictable. Asking the model to argue against a position to surface counterarguments and weaknesses in the reasoning presented. Overloading the prompt with excessive context, which can confuse the model and degrade response quality and relevance.

Question 31 Prompt Engineering

What is 'dynamic prompting'?

Allocating context window tokens strategically between instructions, examples, context, and expected output space. Using instructions like "Be concise" or "Provide a detailed explanation" to regulate response length and depth. Asking the model to first create an outline (skeleton), then flesh out each part for speed and parallel processing. Constructing prompts programmatically at runtime by inserting variable data into templates for flexible, reusable prompts.

Question 32 Prompt Engineering

What is top-p (nucleus) sampling?

A chunk of text (roughly 4 characters or 3/4 of a word) that the model processes as a single unit. Prompting the model to generate an answer, then critique it, then produce an improved version based on the critique. A sampling method where the model considers only the smallest set of tokens whose cumulative probability exceeds the value p. Prefacing a question with "As an expert in X" or "From the perspective of a Y" to elicit domain-specific expertise.

Question 33 Prompt Engineering

What is 'analogical prompting'?

Providing exactly one example of the desired input-output pattern before asking the model to complete a new task. The practice of designing, crafting, and optimizing input prompts to elicit desired outputs from AI language models. Asking the model to perform a task without providing any examples, relying solely on the model's pre-trained knowledge. Asking the model to draw analogies from related domains to help solve a new or unfamiliar problem.

Question 34 Prompt Engineering

What is self-consistency prompting?

Asking the model to check its own output against specified criteria before returning a final answer to the user. Placing the most important instruction both at the beginning and end of a long prompt to reduce position bias effects. Generating multiple reasoning paths for the same problem and selecting the most common answer to improve accuracy. Combining zero-shot prompting with "Let's think step by step" to get reasoning without needing to provide examples.

Question 35 Prompt Engineering

What is negative prompting?

Being precise and explicit about what you want: the task, format, length, tone, and any constraints on the response. Prompting the model to generate multiple different solutions or approaches to increase option diversity and creativity. Providing relevant background information helps the model understand the task better and give more accurate, relevant responses. Telling the model explicitly what NOT to do (e.g., "Do not use jargon") to constrain its output style or content.

Question 36 Prompt Engineering

What does 'specificity' mean in prompt design?

A prompt that gives the model a goal, available tools, and autonomy to plan and execute multi-step actions independently. Being precise and explicit about what you want: the task, format, length, tone, and any constraints on the response. A security attack where malicious instructions are embedded in user input to override or hijack the model's original instructions. Generating multiple reasoning paths for the same problem and selecting the most common answer to improve accuracy.

Question 37 Prompt Engineering

What is prompt engineering?

The practice of designing, crafting, and optimizing input prompts to elicit desired outputs from AI language models. Processing long documents in chunks, prompting each chunk separately, then synthesizing the individual results together. Delimiters prevent prompt injection, reduce ambiguity, and help the model distinguish between instructions and data clearly. A technique where the model generates an answer, creates verification questions about it, then checks each one independently.

Question 38 Prompt Engineering

What is in-context learning?

Asks the model to first identify the abstract principle behind a question before answering the specific question. Ask the model to cite sources, say "I don't know" when uncertain, or provide reference text for it to draw from directly. The model's ability to learn a new task purely from examples provided in the prompt, without any weight updates. Asking the model to explain a concept in simple terms to verify understanding, similar to explaining to a rubber duck.

Question 39 Prompt Engineering

What is 'parallel prompting'?

Asking the model to return data in a structured format like JSON, XML, or CSV for programmatic use downstream. Explicitly stating the desired output format (e.g., "Respond in JSON with keys: name, age, city") in the prompt. A model's ability to follow explicit, structured directions provided in a prompt without needing examples to demonstrate the task. Sending multiple prompt variations simultaneously and selecting the best output, useful for optimization and A/B testing.

Question 40 Prompt Engineering

What is one-shot prompting?

Structuring the prompt as a conversation with alternating user/assistant turns to provide context and guide the response. Generating responses that explicitly reference and stay faithful to provided source documents rather than general knowledge. Providing exactly one example of the desired input-output pattern before asking the model to complete a new task. The phenomenon where small changes in wording significantly affect model outputs, making prompts fragile and unpredictable.

Question 41 Prompt Engineering

What is 'ensemble prompting'?

Providing relevant background information helps the model understand the task better and give more accurate, relevant responses. Running the same prompt multiple times and aggregating results (e.g., by voting) to improve reliability and reduce variance. Specifying the exact structure of the response (JSON, markdown, bullet list, table) to make outputs easier to parse and use. The message sent by the human in a conversation with an AI model, expressing a request, question, or task.

Question 42 Prompt Engineering

What is zero-shot chain-of-thought prompting?

A minimal, core prompt used as the starting point for generating or expanding into a full set of variations. To clearly separate different parts of a prompt (instructions, context, examples) and reduce ambiguity for the model. A technique where the model generates an answer, creates verification questions about it, then checks each one independently. Combining zero-shot prompting with "Let's think step by step" to get reasoning without needing to provide examples.

Question 43 Prompt Engineering

What is output length control in prompting?

Asking the model to perform a task without providing any examples, relying solely on the model's pre-trained knowledge. Specifying a word count, sentence count, or character limit to constrain the model's response to the appropriate length. A technique by Anthropic where the model critiques and revises its own outputs based on a set of defined principles. Providing examples of the desired output is often more effective than describing the format in words alone.

Question 44 Prompt Engineering

What is task decomposition in prompting?

Breaking a large, complex task into smaller, more manageable sub-tasks that are prompted and solved individually. The phenomenon where small changes in wording significantly affect model outputs, making prompts fragile and unpredictable. Asking the model to argue against a position to surface counterarguments and weaknesses in the reasoning presented. Asking the model to explain a concept in simple terms to verify understanding, similar to explaining to a rubber duck.

Question 45 Prompt Engineering

What is ReAct prompting?

Sending multiple prompt variations simultaneously and selecting the best output, useful for optimization and A/B testing. A technique where the model generates an answer, creates verification questions about it, then checks each one independently. A framework combining Reasoning (thinking) and Acting (using tools) in interleaved steps to solve multi-step tasks. A prompt is the input text or instruction given to an AI language model to guide its response.

Question 46 Prompt Engineering

What is meta-prompting?

Asks the model to first identify the abstract principle behind a question before answering the specific question. Using a model to generate or improve prompts for another model or for itself. Explicitly stating the desired output format (e.g., "Respond in JSON with keys: name, age, city") in the prompt. A directive tells the model what to do; a query asks it a question. Directives often yield more controlled, structured outputs.

Question 47 Prompt Engineering

Why use delimiters in prompts?

Progressively loosening overly strict prompt constraints when the model fails to generate satisfactory outputs. Adjusting prompt wording to ensure the model's confidence levels match actual accuracy rates for reliable outputs. Including prior conversation turns in the prompt so the model can maintain coherent multi-turn dialogue across exchanges. Delimiters prevent prompt injection, reduce ambiguity, and help the model distinguish between instructions and data clearly.

Question 48 Prompt Engineering

What is 'prompt injection defense'?

A sampling method where the model considers only the smallest set of tokens whose cumulative probability exceeds the value p. Techniques like input sanitization, XML tags, and clear instruction separation to protect against prompt injection attacks. Tracking changes to prompts over time (like code versioning) to enable reproducibility, rollback, and A/B comparison. A prompt is the input text or instruction given to an AI language model to guide its response.

Question 49 Prompt Engineering

What is 'tool-use prompting'?

Use higher temperature (0.7–1.0) for creative tasks where variety and novelty are desired. Breaking a large, complex task into smaller, more manageable sub-tasks that are prompted and solved individually. Instructing the model to call external tools (APIs, code runners, search engines) as part of its reasoning and action loop. Placing the most important instruction both at the beginning and end of a long prompt to reduce position bias effects.

Question 50 Prompt Engineering

What is 'summarization chaining'?

A parameter (0–1 or higher) controlling randomness: lower values give more deterministic outputs, higher values give more creative responses. Breaking a long document into sections, summarizing each section, then summarizing the summaries hierarchically. A technique that decomposes a complex problem into sub-problems and solves them in order from simplest to most complex. A chunk of text (roughly 4 characters or 3/4 of a word) that the model processes as a single unit.