Generative AI Leader Techniques to Improve Generative AI Model Output — Timed 50 Questions

Question 1

A team is building a generative AI model for customer support. They notice the model often produces overly polite but unhelpful responses. Which technique would best improve response quality without sacrificing helpfulness?

Accepted Answer

Apply reinforcement learning from human feedback (RLHF). RLHF directly addresses the misalignment between the model's training objective (e.g., predicting the next token) and the desired outcome (helpful, not just polite). By using human feedback to train a reward model, the system learns to optimize for response quality and helpfulness, reducing sycophantic or overly polite but uninformative outputs.

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Increase the amount of training data

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Lower the top_k sampling value

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Increase the temperature parameter

Question 2

A generative AI model for code generation sometimes produces syntactically incorrect code. The team wants to reduce syntax errors without retraining the entire model. Which approach is most effective?

Accepted Answer

Implement constrained decoding with grammar rules. Constrained decoding with grammar rules directly enforces the syntax of the target programming language during token generation, preventing the model from producing invalid constructs. This approach modifies the decoding process (e.g., using a context-free grammar or a formal syntax specification) to mask or forbid tokens that would lead to a syntax error, without altering the underlying model weights. It is the most effective method because it guarantees syntactically correct output at generation time, rather than relying on post-hoc fixes or probabilistic adjustments.

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Run a syntax checker after generation and regenerate

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Add a system prompt that instructs the model to produce valid code

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Increase beam search width

Question 3

A company uses a text-to-image model to generate marketing visuals. The outputs often contain distorted human faces. Which technique is most likely to improve face generation?

Accepted Answer

Fine-tune the model on a curated dataset of human faces. Fine-tuning the model on a high-quality dataset of human faces directly addresses the distortion issue. Option B is wrong because increasing inference steps may improve image quality but not specifically faces. Option C is wrong because reducing CFG scale reduces adherence to the prompt, not face quality. Option D is wrong because increasing image size might not fix distortion.

Answer

Increase the output resolution

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Increase the number of inference steps

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Reduce the classifier-free guidance scale

Question 4

A team is deploying a large language model for legal document summarization. They find the model occasionally omits critical legal clauses. Which improvement technique would be most effective?

Accepted Answer

Design a prompt that explicitly lists required sections. Using prompt engineering with explicit instructions to include all clauses and possibly a checklist directly addresses omissions. Option A is wrong because fine-tuning would require labeled data of summaries with clauses. Option B is wrong because temperature reduction might make output less creative but doesn't enforce completeness. Option D is wrong because it adds randomness, making omissions more likely.

Answer

Increase the top_p value to 1.0

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Fine-tune the model on legal summaries

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Lower the temperature to 0.1

Question 5

A generative AI model for chatbot responses sometimes produces toxic language. The team wants to reduce toxicity without significantly affecting the model's helpfulness. Which approach is best?

Accepted Answer

Fine-tune with a dataset of non-toxic responses and use RLHF. Fine-tuning with a curated dataset of non-toxic responses directly adjusts the model's weights to reduce the likelihood of generating toxic language, while RLHF (Reinforcement Learning from Human Feedback) further aligns the model with human preferences for helpfulness and safety. This combined approach addresses the root cause of toxicity in the model's behavior without the blunt trade-offs of other methods, preserving the model's utility.

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Increase the temperature parameter

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Reduce the maximum output tokens

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Apply a toxicity classifier as a post-processing filter

Question 6

A team notices their text generation model repeats phrases excessively. Which technique would most directly reduce repetition?

Accepted Answer

Apply a repetition penalty of 1.2. Using a repetition penalty during decoding discourages the model from repeating tokens. Option A is wrong because it increases randomness, which might reduce repetition but could also reduce coherence. Option B is wrong because beam search can increase repetition. Option D is wrong because temperature reduction makes output more deterministic, potentially increasing repetition.

Answer

Use beam search with a beam width of 5

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Increase top_k to 100

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Lower temperature to 0.5

Question 7

A company uses a generative model to produce product descriptions. The descriptions are factually inconsistent with the product specs. Which technique would best ensure factual accuracy?

Accepted Answer

Implement retrieval-augmented generation (RAG) with product database. Retrieval-augmented generation (RAG) is the best technique because it dynamically retrieves relevant, up-to-date product specifications from a trusted database at inference time, grounding the model's output in verified facts. This directly addresses factual inconsistency by ensuring the generated description is based on authoritative source data rather than relying solely on the model's parametric memory.

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Enhance the system prompt with product details

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Lower the temperature to 0.0

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Fine-tune the model on product descriptions

Question 8

A team is fine-tuning a large language model for medical advice. Which TWO techniques are most effective for improving the safety and reliability of the model's outputs?

Accepted Answer

Constitutional AI. Constitutional AI (A) is correct because it embeds a set of ethical principles directly into the model's training process, allowing the model to self-critique and revise its outputs to avoid harmful or unsafe medical advice. This technique proactively enforces safety constraints without requiring extensive human labeling, making it highly effective for high-stakes domains like healthcare.

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Lowering the temperature to 0.0

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Increasing training data size

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Increasing top_p to 1.0

Question 9

A team wants to reduce hallucinations in a question-answering model. Which THREE techniques should they consider?

Accepted Answer

Fine-tune the model on a curated factual dataset. Fine-tuning on a curated factual dataset directly adjusts the model's weights to prioritize accurate, domain-specific knowledge, reducing the likelihood of generating unsupported or hallucinated content. This technique anchors the model's output in verified data, making it more reliable for question-answering tasks.

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Reduce the number of tokens in output

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Increase the temperature parameter

Question 10

Which TWO techniques are commonly used to control the style and tone of a generative model's output?

Accepted Answer

Fine-tuning on a dataset with desired style. Option C is correct because fine-tuning on a dataset that embodies the desired style directly adjusts the model's weights, making it consistently produce outputs with that specific tone and style. This is a fundamental technique for customizing generative models, as it teaches the model the exact patterns, vocabulary, and stylistic nuances present in the training data.

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Adjusting the temperature

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Modifying the top_k value

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Changing the top_p value

Question 11

Refer to the exhibit. The team changed the generation parameters to reduce output variability. However, summaries now often repeat the same phrases. Which parameter change is most likely causing the repetition?

Accepted Answer

Reducing temperature from 0.7 to 0.2. Lowering temperature to 0.2 makes the model more deterministic, increasing repetition. Option A is wrong because top_k reduction also contributes to determinism. Option B is wrong because top_p reduction also narrows token selection. Option D is wrong because the model is the same.

Answer

Reducing top_p from 0.95 to 0.85

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Using the same model text-bison@002

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Reducing top_k from 40 to 10

Question 12

Refer to the exhibit. The endpoint is experiencing high latency during traffic spikes. The team wants to improve response time by reducing queueing. Which change to the configuration would be most effective?

Accepted Answer

Increase maxReplicaCount to 10. Increasing maxReplicaCount to 10 allows the autoscaler to provision more replicas during traffic spikes, distributing the incoming requests across additional endpoints. This directly reduces queueing at each replica because the load is spread over more instances, lowering per-instance latency. The change targets the root cause—insufficient capacity to handle peak load—rather than adjusting thresholds or model versions.

Answer

Decrease minReplicaCount to 0

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Change the model version to '2'

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Decrease the target value in autoscaling metric to 50

Question 13

A data science team is fine-tuning a large language model using Vertex AI to generate marketing copy. They notice that the generated text is often repetitive and lacks creativity. Which technique should they apply to improve output diversity?

Accepted Answer

Increase the temperature parameter to 0.9.. Increasing the temperature parameter to 0.9 raises the randomness of the probability distribution over tokens, allowing less likely tokens to be selected. This directly counteracts repetitive output by encouraging the model to explore more diverse word choices, which is a standard technique for improving creativity in text generation.

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Decrease the beam search width to 1.

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Decrease the top-k sampling threshold.

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Add more examples of repetitive text to the training dataset.

Question 14

A team is using Vertex AI Pipelines to deploy a generative AI model for real-time inference. The model sometimes generates harmful content. They want to implement a safety filter that checks the output before returning it to the user, but they need to minimize latency. Which approach best balances safety and performance?

Accepted Answer

Use a secondary lightweight classifier to filter outputs in real-time.. Option A is correct because deploying a secondary lightweight classifier (e.g., a distilled BERT or a small logistic regression model) as a post-processing filter allows real-time inference with minimal latency overhead. This approach decouples safety from the primary generative model, enabling fast rejection of harmful outputs without retraining or blocking the main inference pipeline.

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Retrain the model on every flagged harmful output.

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Manually review all outputs before delivery.

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Disable safety checks to improve latency.

Question 15

A developer is using the Gemini API to build a chatbot. They want the model to always respond in a friendly, professional tone. Which prompt engineering technique should they use?

Accepted Answer

Set system instructions to 'You are a friendly and professional assistant.'. Option A is correct because setting system instructions is the most direct and reliable way to define the model's persona and behavioral constraints. In the Gemini API, system instructions act as a persistent, top-level directive that influences every response, ensuring the chatbot consistently adopts a friendly and professional tone without requiring repeated examples or parameter tuning.

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Include a few-shot example in every user message.

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Set the temperature to 0.2.

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Set max output tokens to 100.

Question 16

A team is using a pre-trained language model to summarize legal documents. They find that summaries often miss key dates and parties involved. Which technique would most effectively improve factual accuracy?

Accepted Answer

Fine-tune the model on a dataset of legal summaries with annotated key entities.. Fine-tuning on a dataset of legal summaries with annotated key entities directly teaches the model to recognize and reproduce critical factual elements like dates and parties. This supervised learning approach adjusts the model's weights to prioritize entity extraction and accurate generation, which is the most effective method for improving factual accuracy in domain-specific tasks.

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Use top-p sampling with a low p value.

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Increase the temperature parameter.

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Use chain-of-thought prompting.

Question 17

A company is deploying a generative AI model for customer support. They want to reduce hallucinations while maintaining fluency. They have a large dataset of previous support conversations. Which strategy should they prioritize?

Accepted Answer

Implement retrieval-augmented generation (RAG) using the conversation dataset as a knowledge base.. Retrieval-augmented generation (RAG) directly addresses hallucinations by grounding the model's responses in factual, retrieved data from the conversation dataset. This approach allows the model to generate fluent, contextually relevant answers while reducing the risk of inventing information, as it retrieves actual support interactions as evidence before generating a response.

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Increase the beam search width to 10.

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Fine-tune the model on the conversation dataset.

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Set the temperature to 0.1.

Question 18

A developer is using Vertex AI Studio to test prompts for a text generation model. They want the model to follow a specific output format (JSON). Which prompt engineering approach is most effective?

Accepted Answer

Include a few-shot example of the exact JSON format in the prompt.. Option B is correct because including a few-shot example of the exact JSON format in the prompt provides the model with a concrete pattern to follow, which is the most reliable method for enforcing structured output in generative models. Few-shot prompting leverages in-context learning, where the model uses the provided example to infer the desired schema and formatting rules, reducing ambiguity and improving adherence to the specified JSON structure.

Answer

Set stop sequences to '}'.

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Set the system instruction to 'Always output JSON.'

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Set temperature to 0 to make output deterministic.

Question 19

Which TWO techniques can help improve the factual accuracy of a language model's outputs? (Choose two.)

Accepted Answer

Fine-tune on a domain-specific curated dataset.. Fine-tuning on a domain-specific curated dataset (C) directly adjusts the model's weights using high-quality, verified examples, teaching it to produce factually correct outputs for that domain. This reduces hallucinations by grounding the model in accurate, relevant data rather than relying solely on its pre-training distribution.

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Decrease the max output tokens.

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Increase the temperature parameter.

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Use top-k random sampling.

Question 20

Which THREE approaches are effective for reducing bias in generative model outputs? (Choose three.)

Accepted Answer

Use a balanced training dataset.. Option C is correct because a balanced training dataset reduces the risk of the model learning spurious correlations or skewed distributions that lead to biased outputs. By ensuring that all demographic groups, topics, or perspectives are represented proportionally, the model's learned probability distribution is less likely to favor one group over another, directly mitigating representation bias at the data level.

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Set temperature to a very high value.

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Use adversarial training.