Medium Gen AI Leader practice questions

A retail company wants to customize a large foundation model to consistently follow their internal product-naming conventions and writing style. Their ML team has a modest compute budget and wants to avoid the cost and time of retraining all of the model's parameters. They also want to keep the original base model intact so it can still serve other general tasks. Which customization approach best fits these constraints?

A large insurance company has deployed a gen AI assistant to help claims adjusters draft case summaries. Six months later, leadership finds that only 15% of adjusters use the tool regularly. Surveys reveal that many employees fear the tool will replace their jobs and are unsure how it fits into their existing workflow. Which action should the leadership team prioritize to improve adoption?

A retail company deploys a generative AI assistant trained largely on historical customer support transcripts from one region. After launch, leaders notice the assistant frequently uses idioms and cultural references unfamiliar to customers in other regions, causing confusion. Which responsible AI concern does this situation most directly illustrate?

A financial analyst is using a gen AI model to solve multi-step word problems involving budget allocations and percentage calculations. The model often produces the correct setup but arrives at the wrong final number, jumping directly to an answer without showing intermediate steps. Which prompt engineering technique is most likely to improve the accuracy of these calculations?

A financial analyst uses a gen AI model to solve multi-step budget allocation word problems. When she asks the model to give the final answer directly, it frequently makes arithmetic and logic errors. She wants to improve accuracy without collecting labeled examples or tuning the model. Which prompt engineering technique should she apply?

A retail company deploys a customer-service chatbot built on a large language model. During testing, an employee asks about the return policy, and the chatbot confidently states that customers have 90 days to return items — but the actual company policy is 30 days, and this number appears nowhere in the model's training data or prompt. Which gen AI concept does this behavior best illustrate?

A marketing team uses a gen AI model to generate product descriptions. They have written three different prompt versions and want to systematically determine which one produces the highest-quality descriptions before rolling one out to production. What is the most effective way to evaluate which prompt performs best?

A product team is building a customer-support assistant on a large language model. During long conversations, the assistant starts to 'forget' details the customer mentioned near the beginning of the chat. An engineer explains this is due to a limit on how much text the model can consider at once. Which core gen AI concept is the engineer describing?

A product analyst pastes a very long transcript into a gen AI assistant and asks for a summary. The tool responds but appears to have ignored the earliest parts of the transcript, summarizing only the later sections. What is the most likely cause?

A financial services firm wants to fine-tune a gen AI assistant on its internal documents, which include a mix of public marketing material, confidential client records, and regulated PII. Before any data is used for training, the Chief Data Officer wants to ensure the firm avoids inadvertently exposing sensitive information through the model. Which governance step should be completed FIRST?

A retail company wants to build a gen AI assistant that answers employee questions using data from its HR system, finance platform, and internal wiki. During discovery, the project lead finds that the three systems use inconsistent formats, contain many duplicate and outdated records, and are managed by separate teams with no shared access policy. Leadership is eager to launch quickly. What should the project lead prioritize FIRST to set the initiative up for success?

A marketing analyst writes a prompt that includes both instructions ('Summarize the following customer review in one sentence') and the actual review text, all run together in a single paragraph. The model sometimes treats parts of the review as new instructions, producing inconsistent summaries. Without changing models or adding training data, what is the simplest prompt engineering improvement to make the output more reliable?

A product team is explaining to executives how their new gen AI feature can group customer feedback by meaning rather than exact keywords. They want to describe the underlying concept that lets the system understand that 'the app keeps crashing' and 'it freezes constantly' are semantically similar. Which core generative AI concept are they describing?

A financial services company wants to build an internal tool that lets employees search a large knowledge base using natural language. When someone asks 'How do I reset a locked customer account?', the tool should return documents that are conceptually related even if they use different wording, such as 'unlock a client profile.' Which generative AI concept most directly enables this semantic matching between the query and the documents?

A product team has deployed a gen AI assistant that drafts customer email responses. Leadership wants a reliable way to measure whether output quality is actually improving as the team iterates on prompts and grounding. Automated metrics alone haven't correlated well with what customers consider a 'good' reply. What is the most effective approach to evaluate and improve output quality in this situation?

A product team at an insurance company has deployed a gen AI assistant that answers policy questions. They frequently adjust prompts to improve responses, but they worry that a change fixing one type of question might silently break answers to other questions. What practice should they adopt to reliably measure whether each prompt change improves overall output quality?

A media company deploys a gen AI tool that summarizes news articles. Early users complain that some summaries are fluent and well-written but occasionally contradict details in the original article. The product team wants to measure this specific problem systematically before their next model update. Which evaluation approach most directly targets the issue they are seeing?

A marketing team uses a general-purpose LLM to generate product taglines. When they simply ask 'Write a tagline for our eco-friendly water bottle,' the outputs are generic and inconsistent in tone. The team wants better results quickly, without collecting a large dataset, training a custom model, or connecting to any external data source. What is the most appropriate technique to improve output quality in this situation?

A financial analyst uses a gen AI model to extract key risk factors from lengthy 10-K filings. With a simple zero-shot instruction, the model produces inconsistent output — sometimes bullet lists, sometimes paragraphs, and occasionally it invents risks not present in the document. The analyst wants to improve consistency and accuracy without the cost and time of collecting a large labeled dataset or standing up new infrastructure. What is the most appropriate FIRST technique to try?

A support operations manager wants a gen AI model to categorize incoming customer tickets into one of five predefined categories using a very specific label format (e.g., 'CAT-3: Billing'). Early tests with a plain instruction produce inconsistent formatting and occasional made-up categories. The manager cannot train or tune a model and needs a quick fix within the prompt itself. Which prompt engineering technique should they apply first?

A marketing analyst wants a foundation model to generate product descriptions that follow the company's very specific style: a punchy one-line headline, exactly three bullet points, and a closing call-to-action. When they simply describe these rules in a plain instruction, the model's output is inconsistent — sometimes it skips the call-to-action or writes four bullets. The analyst does not have a labeled dataset and needs a quick fix without any training. Which prompt engineering technique should they apply first?

A marketing team wants a gen AI model to rewrite product descriptions in their distinctive brand voice. They only have about 15 well-written examples of the desired style, no ML engineers, and need a working solution within a day for a low-volume campaign. Which approach best fits these constraints?

A product team uses a foundation model to convert customer feedback into structured JSON with fields for sentiment, category, and priority. Using a simple instruction prompt, the model returns correct content but with inconsistent key names, varying capitalization, and occasionally missing fields. The team cannot change the model and wants a fast prompt-only fix that enforces a consistent output structure. What should they do?

A financial services company needs its gen AI assistant to consistently produce loan summaries in a highly specific internal format and tone that the base model struggles to replicate, even after extensive prompt engineering with detailed examples in every prompt. They have thousands of high-quality example summaries written by their analysts. Which technique will most reliably bake this behavior into the model so it no longer needs lengthy examples in each prompt?

A healthcare company uses a foundation model to generate patient-facing message drafts. Despite detailed prompts and several examples, the model still fails to consistently adopt the specialized empathetic clinical tone and terminology the organization requires across thousands of daily requests. They have a large curated dataset of high-quality past messages written by their clinical staff. Which approach will most reliably embed this consistent style and vocabulary into the model's default behavior?