Module 10 self-check | AI GRCA Practitioner Programme

Why might an outdated document being used in a chatbot's answer be worse than the chatbot admitting it doesn't know?

Stale information is never actually a real riskIt isn't worse — outdated confident answers are always preferableA confidently outdated answer can mislead a user into acting on wrong information without any signal that verification is neededUsers always check the document date themselves

'Source quality' as a RAG risk refers to:

The visual design of the source documentOnly the file format usedHow fast a document loadsWhether the underlying documents being retrieved are accurate, current and trustworthy

A cited source doesn't actually support the claim made in the answer. This is a failure of:

Grounding and citation traceabilityDatabase rightsPurpose limitationData minimisation

'Overreliance' as an LLM risk means:

The model is used too rarelyUsers trusting AI-generated output without appropriate scrutiny or verificationThe model requires too much computeA synonym for prompt injection

Between a small, single-purpose model and a large, general-purpose model, which factor most matters for a narrow, well-defined task?

A smaller, purpose-built model can often perform as well or better on a narrow task with lower cost and easier evaluationSmall models cannot be evaluated for accuracyLarge models are always cheaper to runAlways choose the largest model regardless of task

In LLM application architecture, what's the difference between a system prompt and a user prompt?

They are identical and interchangeableThe system prompt sets persistent behaviour/instructions set by the application; the user prompt is the live input from the person using itUser prompts are always hidden from the modelSystem prompts are only used in RAG systems

Why is 'source traceability' particularly important in a compliance or legal RAG use case?

Traceability is irrelevant to compliance workBeing able to trace a stated conclusion back to a specific authoritative source is often essential for defensibilityCompliance use cases never require citationsTraceability only matters for marketing content

A vector database returns technically similar but contextually wrong content for a query. What RAG risk does this best illustrate?

Toxic outputFine-tuning failureAccess-control failureIncorrect retrieval, where similarity doesn't guarantee true relevance

GenAI, RAG and LLM Application Risk