2 exercises — articulate AI limitations and risks clearly to executives, product managers, and customers.
0 / 19 completed
1 / 19
A colleague asks: "Why can't we just trust the AI — it's been trained on everything?" Which response best articulates the hallucination risk to a non-technical stakeholder?
Option B is the professional explanation. It:
• Explains the mechanism — not just "it makes mistakes" but why: token prediction optimises for plausibility, not truth • Names the risk precisely — "high confidence presentation of incorrect information" (hallucination) • Proposes a mitigation — "define a verification step" • Contextualises by domain — code, legal, financial domains where errors are especially costly
How to explain AI bias as a second risk: "LLMs reflect patterns in their training data. If that data over-represents certain groups, languages, or perspectives, the model's outputs will too. This matters for: hiring tools (may encode historical biases), user-facing content (may work better in English than other languages), and recommendation systems."
The key communication skill: translate "the model hallucinates" into business risk language: "Without a human verification step, we risk publishing or acting on incorrect information presented with false confidence."
2 / 19
Your CTO asks: "What are the realistic limitations we should communicate to customers about our new AI feature?" Which answer is most complete and honest?
Option C is the professional answer. It provides three specific, actionable limitations with context. This is the standard structure for communicating AI limitations to stakeholders:
1. Knowledge cutoff — every LLM has a training cutoff date. Mitigations: RAG for up-to-date data, clearly displaying the knowledge date in UI.
2. Hallucination rate — the rate varies by task (high for obscure facts, lower for common knowledge). Mitigations: confidence scores (if available), retrieval grounding, human review for high-stakes outputs.
3. Domain and language bias — models trained on English-heavy data underperform in other languages. Mitigations: fine-tuning on target language data, evaluation in the target language.
The communication principle: pair each limitation with a mitigation or design choice. "This is a risk" with no proposed action sounds like an alarm, not an analysis.
3 / 19
Reviewer: 'This function seems overly reliant on the AI's suggestion. I'm concerned about potential biases creeping in if it's consistently used without human oversight. What's the best way to address this during the code review?
The key here is framing the discussion for a code review setting. Option A misunderstands the core risk of bias in AI – it's not just about convenience. Option B offers a superficial solution that doesn't address the underlying problem. Option D is too extreme and misses the opportunity to engage with the suggestion constructively. Option C, however, correctly identifies the need for investigation into the AI's reasoning and data sources, aligning with responsible development practices when using AI tools.
4 / 19
PR Description
During a code review of a new feature utilizing an AI-powered suggestion engine for generating SQL queries, you notice the team lead has submitted this PR description: 'Automated SQL query generation – leveraging AI for efficiency!'
Which revision to this description best communicates potential risks and encourages further discussion with the AI provider regarding data quality and validation?
The original description is too simplistic and doesn't adequately address the core risk: the AI's suggestions are only as good as the data it was trained on. Option A is too weak because it simply states the description lacks detail without explaining *why* that lack of detail is problematic. While option B is technically correct in stating it's acceptable, it ignores the significant potential for errors and doesn't encourage further investigation. Option D correctly identifies a key issue – biased or inaccurate training data – but misses the opportunity to prompt a conversation about validation processes.
5 / 19
PR Description: 'Automated SQL query generation – leveraging AI for efficiency!'
The team lead wants to highlight potential risks and prompt a deeper conversation with the AI provider. Which revision of this description is most effective in achieving these goals? A. 'AI-powered SQL generation – streamlining development.' B. 'Automated SQL query generation – leveraging AI for efficiency, while ensuring data accuracy and validation against our existing database schema.' C. 'This feature utilizes an AI to generate SQL queries; further investigation into the AI's training data is recommended.' D. 'Optimizing SQL query creation with AI – a key step in improving performance metrics.'
The original description is too simplistic and doesn't acknowledge potential risks. Option B directly addresses concerns by explicitly mentioning 'data accuracy' and 'validation,' showing an understanding of the need for oversight. Options A and D are overly promotional and gloss over critical aspects. Option C highlights a necessary investigation, but lacks the proactive framing needed to initiate a discussion with the AI provider – it focuses on *their* responsibility rather than the team's.
6 / 19
Reviewer: 'This function seems overly reliant on the AI's suggestion. I'm concerned about potential biases creeping in if it's consistently used without human oversight. What's the best way to address this during the code review?
The key here is framing the discussion for a code review setting. Option A misunderstands the core risk of bias in AI – it's not just about convenience. Option B offers a superficial solution that doesn't address the underlying problem. Option D is too extreme and misses the opportunity to engage with the suggestion constructively. Option C, however, correctly identifies the need for investigation into the AI's reasoning and data sources, aligning with responsible development practices when using AI tools.
7 / 19
PR Description
During a code review of a new feature utilizing an AI-powered suggestion engine for generating SQL queries, you notice the team lead has submitted this PR description: 'Automated SQL query generation – leveraging AI for efficiency!'
Which revision to this description best communicates potential risks and encourages further discussion with the AI provider regarding data quality and validation?
The original description is too simplistic and doesn't adequately address the core risk: the AI's suggestions are only as good as the data it was trained on. Option A is too weak because it simply states the description lacks detail without explaining *why* that lack of detail is problematic. While option B is technically correct in stating it's acceptable, it ignores the significant potential for errors and doesn't encourage further investigation. Option D correctly identifies a key issue – biased or inaccurate training data – but misses the opportunity to prompt a conversation about validation processes.
8 / 19
PR Description: 'Automated SQL query generation – leveraging AI for efficiency!'
The team lead wants to highlight potential risks and prompt a deeper conversation with the AI provider. Which revision of this description is most effective in achieving these goals? A. 'AI-powered SQL generation – streamlining development.' B. 'Automated SQL query generation – leveraging AI for efficiency, while ensuring data accuracy and validation against our existing database schema.' C. 'This feature utilizes an AI to generate SQL queries; further investigation into the AI's training data is recommended.' D. 'Optimizing SQL query creation with AI – a key step in improving performance metrics.'
The original description is too simplistic and doesn't acknowledge potential risks. Option B directly addresses concerns by explicitly mentioning 'data accuracy' and 'validation,' showing an understanding of the need for oversight. Options A and D are overly promotional and gloss over critical aspects. Option C highlights a necessary investigation, but lacks the proactive framing needed to initiate a discussion with the AI provider – it focuses on *their* responsibility rather than the team's.
9 / 19
Reviewer: 'This function seems overly reliant on the AI's suggestion. I'm concerned about potential biases creeping in if it's consistently used without human oversight. What's the best way to address this during the code review?
The key here is framing the discussion for a code review setting. Option A misunderstands the core risk of bias in AI – it's not just about convenience. Option B offers a superficial solution that doesn't address the underlying problem. Option D is too extreme and misses the opportunity to engage with the suggestion constructively. Option C, however, correctly identifies the need for investigation into the AI's reasoning and data sources, aligning with responsible development practices when using AI tools.
10 / 19
PR Description
During a code review of a new feature utilizing an AI-powered suggestion engine for generating SQL queries, you notice the team lead has submitted this PR description: 'Automated SQL query generation – leveraging AI for efficiency!'
Which revision to this description best communicates potential risks and encourages further discussion with the AI provider regarding data quality and validation?
The original description is too simplistic and doesn't adequately address the core risk: the AI's suggestions are only as good as the data it was trained on. Option A is too weak because it simply states the description lacks detail without explaining *why* that lack of detail is problematic. While option B is technically correct in stating it's acceptable, it ignores the significant potential for errors and doesn't encourage further investigation. Option D correctly identifies a key issue – biased or inaccurate training data – but misses the opportunity to prompt a conversation about validation processes.
11 / 19
PR Description: 'Automated SQL query generation – leveraging AI for efficiency!'
The team lead wants to highlight potential risks and prompt a deeper conversation with the AI provider. Which revision of this description is most effective in achieving these goals? A. 'AI-powered SQL generation – streamlining development.' B. 'Automated SQL query generation – leveraging AI for efficiency, while ensuring data accuracy and validation against our existing database schema.' C. 'This feature utilizes an AI to generate SQL queries; further investigation into the AI's training data is recommended.' D. 'Optimizing SQL query creation with AI – a key step in improving performance metrics.'
The original description is too simplistic and doesn't acknowledge potential risks. Option B directly addresses concerns by explicitly mentioning 'data accuracy' and 'validation,' showing an understanding of the need for oversight. Options A and D are overly promotional and gloss over critical aspects. Option C highlights a necessary investigation, but lacks the proactive framing needed to initiate a discussion with the AI provider – it focuses on *their* responsibility rather than the team's.
12 / 19
Reviewer: 'This function seems overly reliant on the AI's suggestion. I'm concerned about potential biases creeping in if it's consistently used without human oversight. What's the best way to address this during the code review?
The key here is framing the discussion for a code review setting. Option A misunderstands the core risk of bias in AI – it's not just about convenience. Option B offers a superficial solution that doesn't address the underlying problem. Option D is too extreme and misses the opportunity to engage with the suggestion constructively. Option C, however, correctly identifies the need for investigation into the AI's reasoning and data sources, aligning with responsible development practices when using AI tools.
13 / 19
PR Description
During a code review of a new feature utilizing an AI-powered suggestion engine for generating SQL queries, you notice the team lead has submitted this PR description: 'Automated SQL query generation – leveraging AI for efficiency!'
Which revision to this description best communicates potential risks and encourages further discussion with the AI provider regarding data quality and validation?
The original description is too simplistic and doesn't adequately address the core risk: the AI's suggestions are only as good as the data it was trained on. Option A is too weak because it simply states the description lacks detail without explaining *why* that lack of detail is problematic. While option B is technically correct in stating it's acceptable, it ignores the significant potential for errors and doesn't encourage further investigation. Option D correctly identifies a key issue – biased or inaccurate training data – but misses the opportunity to prompt a conversation about validation processes.
14 / 19
PR Description: 'Automated SQL query generation – leveraging AI for efficiency!'
The team lead wants to highlight potential risks and prompt a deeper conversation with the AI provider. Which revision of this description is most effective in achieving these goals? A. 'AI-powered SQL generation – streamlining development.' B. 'Automated SQL query generation – leveraging AI for efficiency, while ensuring data accuracy and validation against our existing database schema.' C. 'This feature utilizes an AI to generate SQL queries; further investigation into the AI's training data is recommended.' D. 'Optimizing SQL query creation with AI – a key step in improving performance metrics.'
The original description is too simplistic and doesn't acknowledge potential risks. Option B directly addresses concerns by explicitly mentioning 'data accuracy' and 'validation,' showing an understanding of the need for oversight. Options A and D are overly promotional and gloss over critical aspects. Option C highlights a necessary investigation, but lacks the proactive framing needed to initiate a discussion with the AI provider – it focuses on *their* responsibility rather than the team's.
15 / 19
You're in a Slack channel discussing the new AI-powered code completion tool. A developer, Alex, says: 'It's amazing! It just solved that complex SQL query in seconds. No need to worry about errors.' What is the MOST appropriate response to gently raise a potential concern without dismissing Alex's enthusiasm?
The best response acknowledges Alex's excitement while introducing a crucial point: AI-generated code isn't infallible. Option A directly addresses the risk of unexpected results, which is a key concern when using an AI tool for complex tasks. Options B and C are overly enthusiastic or dismissive, while option D is completely inappropriate.
16 / 19
During a code review, the reviewer comments: 'This function utilizes the AI's suggestion for handling user input validation. I'm concerned about potential prompt injection attacks if the AI isn't rigorously tested with malicious inputs. What's the best way to mitigate this risk during development?'. Which of these statements BEST reflects how you should proceed?
This scenario highlights prompt injection risks. Option 1 is dangerously dismissive. Option 2 correctly identifies the need for proactive testing and mitigation strategies – specifically input sanitization and adversarial prompting. Options 3 and 4 are incorrect because AI tools aren't inherently secure and require careful oversight.
17 / 19
The team lead submits the following PR description for a new feature utilizing an AI to summarize customer support tickets:
'AI-powered ticket summarization – boosting agent efficiency!'
The product manager asks you to refine this description to accurately communicate potential limitations. Which revision is MOST effective?
Option 1 is overly optimistic and doesn't acknowledge potential issues. Option 2 explicitly states the need for human verification, which is crucial when dealing with AI-generated summaries. Options 3 and 4 are too technical and don't address the core risk of misinterpretation.
18 / 19
During your daily standup, you're discussing progress on a project using an AI to generate documentation. A teammate says: 'We're using the AI to create all our API docs – it's saving us tons of time!' You want to subtly raise concerns about accuracy and consistency. What is the MOST constructive way to respond?
Option 1 simply agrees without addressing potential issues. Option 2 directly raises concerns about accuracy and consistency, which are vital when relying on AI for critical outputs. Options 3 and 4 are overly optimistic or incorrect.
19 / 19
You receive the following API response from an AI model designed to generate SQL queries:
{
"query": "SELECT * FROM users WHERE age > 25;",
"confidence": 0.95,
"explanation": 'This query retrieves all users older than 25.'
}
The team lead wants you to assess the response before deploying it in a production system. What's the MOST important question to ask the AI provider regarding this response?
While confidence scores are useful, they don't guarantee accuracy. Asking about the explanation's thoroughness is critical – ensuring it correctly represents the query and doesn't overlook potential issues or biases. Option 1 focuses solely on a metric without context, while option 3 addresses security concerns that might not be immediately apparent.
What will I practice in "Discussing AI Risks — AI Prompting English Exercise"?
This is an AI Prompting exercise set. It walks through 19 scenario-based multiple-choice questions built around real usage of AI Prompting terminology that IT professionals encounter on the job.
Is this exercise free to use?
Yes. Every exercise on CoderSlingo, including this one, is free to complete with no account, sign-up, or paywall.
How many questions are in this exercise?
This set contains 19 questions. Each one shows immediate feedback and a detailed explanation after you answer, so you learn the correct usage right away rather than waiting for a final score.
Do I need prior experience to complete this exercise?
No prior experience is required. Each question includes a full explanation covering the reasoning behind the correct answer, so the exercise itself teaches the AI Prompting vocabulary as you go.
Can I retry the exercise if I get questions wrong?
Yes — use the "Try again" button on the results screen to reset your answers and go through all the questions again. There is no limit on attempts.
Is my progress saved?
Your answers and score for the current session are tracked in the browser as you go. No account or login is needed, and there is nothing to install.
What if I don't understand a term used in a question?
Read the explanation shown after you answer each question — it breaks down the correct term in plain English with a real-world example. You can also check the site Glossary for quick definitions.
How is this different from reading a blog article on the topic?
Exercises like this one are interactive drills that test and reinforce specific vocabulary through multiple-choice questions, while blog articles explain concepts in prose. Practising here after reading builds active recall, not just passive recognition.
Where can I find more AI Prompting exercises?
See the AI Prompting exercises hub for the full set of related pages, or browse all exercise categories from the main Exercises index.
Can I use this exercise to prepare for a technical interview?
Yes — AI Prompting vocabulary comes up often in technical discussions and interviews. Pair this exercise with our dedicated Interview Preparation section for role-specific practice.