30 of 105 questions · Page 2/2 · Prompt Engineering · Answers revealed
Which TWO parameters directly control the randomness and diversity of generated tokens?
Temperature scales logits to affect randomness.
Why this answer
Temperature and top-p (nucleus sampling) are the primary parameters that influence randomness and diversity.
A developer is using Cohere Command to answer questions grounded in internal technical manuals. They want to ensure the model only answers based on the provided documents and does not use its pre-trained knowledge. Which Cohere-specific technique should be applied?
Cohere supports document-grounded generation where you supply documents and specify they are the only source.
Why this answer
Cohere's document-grounded generation syntax allows you to supply search results or documents and instruct the model to answer solely from those documents, reducing hallucination.
Which prompting technique involves generating multiple independent reasoning paths and then selecting the most common answer?
Self-consistency generates multiple reasoning chains and aggregates the results to increase robustness.
Why this answer
Self-consistency runs chain-of-thought multiple times and aggregates answers (e.g., by majority vote) to improve reliability. The other options are different techniques.
An OCI user is troubleshooting a prompt that sometimes produces outputs containing offensive language. The prompt uses a system prompt to set a professional tone. Which THREE steps should the user take to mitigate this issue? (Select three.)
Frequency penalty reduces token repetition, which can help if offensive language appears repeatedly.
Why this answer
Adding explicit constraints in the system prompt, setting frequency/presence penalties to reduce undesirable patterns, and testing with adversarial inputs are effective safeguards. Using high temperature or removing the system prompt would worsen the problem.
What is the primary benefit of using a system prompt to set the persona and tone before the user message?
System prompts define the model's role and rules for the entire session.
Why this answer
The system prompt establishes persistent behavioral guidelines that influence all subsequent interactions, ensuring consistency without repeating instructions in every user message.
A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?
RAG retrieves relevant document chunks at query time, ensuring the chatbot always answers from the latest uploaded documents without any model retraining.
Why this answer
RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.
Which parameter controls the randomness of the model's output by scaling the probability distribution before sampling?
Temperature adjusts the softmax distribution's sharpness, directly controlling randomness.
Why this answer
Temperature scales the logits before softmax, affecting creativity. Higher values increase randomness; lower values make output more deterministic.
Which prompting technique involves providing the model with a small number of input-output examples within the prompt to guide its behavior?
Few-shot prompting provides a few examples in the prompt to demonstrate the task.
Why this answer
Few-shot prompting includes several examples of the desired input-output mapping to help the model understand the task and output format.
An application uses an LLM to summarize legal documents. The summaries sometimes include hallucinations (details not in the original text). Which prompt engineering technique is MOST effective at reducing hallucinations?
Grounding the model with the source text is the most direct way to reduce hallucination.
Why this answer
Providing the full document context within the prompt (e.g., using a template that includes the document text) grounds the model's response and reduces the chance it invents details. Few-shot examples can also help but are secondary to providing the source.
An AI engineer is designing a prompt to generate a report summary. The prompt currently says: 'Summarize the following text.' The output is often too verbose. Which modification would best enforce a concise, bullet-list format?
This provides explicit format and length constraints.
Why this answer
Explicitly specifying the output format (bullet list, max 5 items) gives the model clear constraints, reducing verbosity.
During iterative refinement, a prompt engineer tests two prompt variants on the same 100 inputs and measures accuracy. Variant A yields 85% accuracy, Variant B yields 82%. However, Variant B's outputs are more concise and preferred by users. What should the engineer do NEXT?
Establishing weighted criteria ensures both dimensions are considered and the decision is objective.
Why this answer
Evaluation should be based on multiple criteria beyond accuracy, especially user preference. The engineer should establish a composite metric that includes both accuracy and conciseness.
Which parameter controls the creativity and randomness of a model's output by adjusting the probability distribution before sampling the next token?
Temperature directly controls the randomness of token selection.
Why this answer
Temperature scales logits before softmax; higher values increase randomness.
A company is building a chatbot that must maintain a professional tone and avoid discussing off-topic subjects. Which TWO prompt engineering approaches should they combine to enforce these requirements?
This directly sets the tone and limits the scope.
Why this answer
A system prompt with role and constraints sets the overall behavior, and a template pattern for responses provides a consistent structure. The other options are less suitable.
A data scientist is using OCI Generative AI to generate synthetic data for training. They observe that the model's outputs lack diversity and often repeat the same phrases. Which combination of parameter adjustments would BEST increase output diversity?
Both higher temperature and higher top-p increase randomness and token variety, boosting diversity.
Why this answer
Increasing temperature and top-p both increase randomness and diversity. Temperature controls the randomness of token selection, while top-p (nucleus sampling) allows a broader set of probable tokens.
A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?
RAG retrieves relevant document chunks at query time, ensuring the chatbot always answers from the latest uploaded documents without any model retraining.
Why this answer
RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.
A practitioner is developing a legal document summarization system and needs to reduce hallucinations. Which prompting technique is most effective for improving factual accuracy by exploring multiple reasoning paths?
Self-consistency samples multiple chain-of-thought outputs and picks the most consistent answer, improving factual accuracy.
Why this answer
Self-consistency generates several reasoning chains and aggregates the results, increasing reliability and reducing hallucinations in tasks requiring factual accuracy.
A prompt engineer is troubleshooting a chatbot that consistently fails to follow instructions when the user includes adversarial input. Which two strategies can mitigate prompt injection attacks? (Choose two.)
Separating instructions from user input prevents the model from treating user input as instructions.
Why this answer
Instruction shielding (clear separation of instruction and input) and input validation/sanitization are effective defenses. Adding more examples or adjusting temperature do not address injection.
Which TWO are benefits of using few-shot prompting compared to zero-shot prompting?
Examples guide the model for tasks it may not have seen frequently.
Why this answer
Few-shot provides examples that improve format adherence and guide the model, especially for complex tasks.
A prompt library manager wants to implement version control for prompt templates used across multiple applications. Which THREE practices should they adopt?
Semantic versioning helps communicate the nature of changes.
Why this answer
Storing templates in a version control system, using semantic versioning, and maintaining a changelog are standard practices for prompt version management. Automated testing is good but not version control per se.
A developer wants the LLM to solve a math problem by reasoning step by step. Which prompting technique should they use?
Chain-of-thought prompts the model to reason step by step, which is ideal for math problems.
Why this answer
Chain-of-thought prompting explicitly instructs the model to show its reasoning steps, improving accuracy on multi-step problems.
When tuning the temperature parameter for a text generation task, which effect does setting temperature to 0.1 have compared to 0.9?
Low temperature reduces randomness, making outputs more deterministic.
Why this answer
Low temperature makes output more deterministic and repetitive; high temperature increases randomness and creativity.
An organization wants to ensure that prompts submitted to an LLM do not contain sensitive customer data. Which practice is most effective?
Correct: input sanitization is a direct mitigation.
Why this answer
Sanitizing prompts before submission (e.g., removing PII, using placeholders) prevents sensitive data from being sent to the model. Other options either do not prevent data leakage or are less direct.
Which TWO are best practices for prompt management in production environments?
A library encourages consistency and saves time.
Why this answer
Versioning and maintaining a library of templates are essential for tracking changes and reusability.
During iterative prompt refinement, a team evaluates two prompt variants on 100 test queries. Variant A scores 85% accuracy but occasionally generates offensive content. Variant B scores 80% accuracy with no safety issues. Which evaluation criterion should take priority for a customer-facing application?
Safety is a hard requirement; accuracy can be improved through further refinement.
Why this answer
For customer-facing applications, safety is paramount. Even if accuracy is slightly lower, ensuring no offensive content is critical to avoid reputational and legal risks. The team should prioritize safety and then work to improve accuracy.
A prompt engineer notices that the model sometimes generates outputs that include parts of the system prompt or user message verbatim. This is likely a symptom of which common prompt failure?
Prompt injection can cause the model to treat parts of the prompt as instructions and output them, leading to leakage.
Why this answer
Prompt injection vulnerabilities can cause the model to leak or repeat the prompt itself. This is a known failure mode where the model confuses the input with output.
In few-shot prompting, what is the primary purpose of including examples in the prompt?
Examples demonstrate the expected mapping from input to output, reducing ambiguity.
Why this answer
Examples guide the model on the desired input-output pattern, improving task performance without fine-tuning.
A prompt engineer is testing two versions of a prompt for a content generation task. They want to measure which version produces more factual and concise outputs. Which evaluation approach is BEST?
A/B testing with multiple inputs and scoring criteria provides objective comparison.
Why this answer
A/B testing with clear metrics (factuality, conciseness) is the standard method for comparing prompt variants. Manual inspection on a few cases is not statistically robust; other options are not comparative.
A user repeatedly gets the same phrase output by the model. Which parameter adjustment is MOST likely to reduce such repetitive patterns?
Frequency penalty penalizes tokens that have been used, reducing repetition.
Why this answer
Frequency penalty reduces the likelihood of repeating tokens that have already appeared, directly combating repetition.
A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?
RAG retrieves relevant document chunks at query time, ensuring the chatbot always answers from the latest uploaded documents without any model retraining.
Why this answer
RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.
An OCI Generative AI user notices that a model generates repetitive phrases when summarizing technical articles. Which parameter adjustment is MOST likely to reduce this repetition?
Frequency penalty penalizes tokens that have already appeared, discouraging the model from repeating phrases.
Why this answer
Frequency penalty reduces the likelihood of repeating tokens that have already appeared, directly targeting repetition. Presence penalty also helps but frequency penalty is stronger for repeated phrases.
Ready to test yourself?
Try a timed practice session using only Prompt Engineering questions.