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Google Generative AI Leader Bundle

Exam Code: Generative AI Leader

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Google Generative AI Leader Practice Test 55

Question 540

The business case is: A customer-help model handles one language well but repeatedly fails another supported language. Select TWO: the appropriate design decision and convincing validation evidence. (Select TWO.)

  1. Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.
  2. A canary release triggers alerts on seeded quality regressions and can restore a known-good configuration.
  3. Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.
  4. Measure business-relevant correctness, retrieval quality, latency, cost and safety on representative held-out cases rather than only demonstrations.
  5. An equity evaluation reports error and outcome metrics by relevant cohort with documented remediation and oversight.

Explanation

Correct Answers

A:

The intervention for the stated situation A customer-help model handles one language well but repeatedly fails another supported language is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly. It directly mitigates: Biased historical records or unequal error rates can lead to harmful decisions even when overall accuracy looks high.

E:

A separate outcome check for the situation A customer-help model handles one language well but repeatedly fails another supported language is: An equity evaluation reports error and outcome metrics by relevant cohort with documented remediation and oversight. It measures the effect of the selected intervention rather than relying on the existence of a proposal.

Incorrect Answers

B:

This choice addresses postdeployment monitoring and drift; its main associated risk is: A model that passed initial acceptance can deteriorate after source, traffic or product changes without visible errors. However, the situation A customer-help model handles one language well but repeatedly fails another supported language is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

C:

This choice addresses data-dependency and poor-source limitations; its main associated risk is: A strong model cannot reliably answer questions whose necessary evidence is missing, contradictory or inaccessible. However, the situation A customer-help model handles one language well but repeatedly fails another supported language is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

D:

This choice addresses evaluation metrics and representative data; its main associated risk is: Optimizing one easy metric can hide incorrect factual claims and failures on rare but important scenarios. However, the situation A customer-help model handles one language well but repeatedly fails another supported language is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

Question 541

A business team evaluating generative AI encounters this situation: A team reports average classification accuracy while a minority customer cohort experiences major errors. It must choose a solution that is useful, secure, properly sourced, and appropriate for its constraints. Which approach is most defensible under these requirements?

  1. Place qualified human review or explicit approval before consequential automated actions and use clear escalation when evidence is weak.
  2. Measure business-relevant correctness, retrieval quality, latency, cost and safety on representative held-out cases rather than only demonstrations.
  3. Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.
  4. Track model, prompt, source-data and tool versions with monitoring for quality drift, security events and changed business outcomes.

Explanation

Correct Answer

C:

The decisive detail is: A team reports average classification accuracy while a minority customer cohort experiences major errors. The appropriate business or technical decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly. The risk avoided is that biased historical records or unequal error rates can lead to harmful decisions even when overall accuracy looks high. Confirm the outcome through: An equity evaluation reports error and outcome metrics by relevant cohort with documented remediation and oversight.

Incorrect Answers

A:

This choice addresses human-in-the-loop for high-impact outputs; its main associated risk is: A generative system given unilateral decision authority can approve unsafe or inaccurate changes based on plausible text. However, the situation A team reports average classification accuracy while a minority customer cohort experiences major errors is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

B:

This choice addresses evaluation metrics and representative data; its main associated risk is: Optimizing one easy metric can hide incorrect factual claims and failures on rare but important scenarios. However, the situation A team reports average classification accuracy while a minority customer cohort experiences major errors is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

D:

This choice addresses postdeployment monitoring and drift; its main associated risk is: A model that passed initial acceptance can deteriorate after source, traffic or product changes without visible errors. However, the situation A team reports average classification accuracy while a minority customer cohort experiences major errors is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

Question 542

A gen AI initiative reports: A team reports average classification accuracy while a minority customer cohort experiences major errors. Which measurement or trial should be required before approval?

  1. An equity evaluation reports error and outcome metrics by relevant cohort with documented remediation and oversight.
  2. A simulated high-impact case pauses with the evidence and only an authorized reviewer can approve its final action.
  3. A canary release triggers alerts on seeded quality regressions and can restore a known-good configuration.
  4. A benchmark reports error types and meaningful KPI changes on normal, edge and adversarial cases with a baseline.

Explanation

Correct Answer

A:

The key acceptance evidence for A team reports average classification accuracy while a minority customer cohort experiences major errors is: An equity evaluation reports error and outcome metrics by relevant cohort with documented remediation and oversight. It checks whether the team actually implemented: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly. A product configuration, demo, or persuasive response alone is not proof.

Incorrect Answers

B:

This choice addresses human-in-the-loop for high-impact outputs; its main associated risk is: A generative system given unilateral decision authority can approve unsafe or inaccurate changes based on plausible text. However, the situation A team reports average classification accuracy while a minority customer cohort experiences major errors is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

C:

This choice addresses postdeployment monitoring and drift; its main associated risk is: A model that passed initial acceptance can deteriorate after source, traffic or product changes without visible errors. However, the situation A team reports average classification accuracy while a minority customer cohort experiences major errors is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

D:

This choice addresses evaluation metrics and representative data; its main associated risk is: Optimizing one easy metric can hide incorrect factual claims and failures on rare but important scenarios. However, the situation A team reports average classification accuracy while a minority customer cohort experiences major errors is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

Question 543

A business team evaluating generative AI encounters this situation: A past loan decision dataset encodes inequitable approvals that a new system would reproduce. It must choose a solution that is useful, secure, properly sourced, and appropriate for its constraints. Which first change most directly addresses the observed failure?

  1. Start with clear instructions, representative examples and grounding before considering costly task-specific model adaptation.
  2. Place qualified human review or explicit approval before consequential automated actions and use clear escalation when evidence is weak.
  3. Treat ungrounded generated assertions as provisional; use reliable source evidence, validation and user-facing uncertainty for factual business answers.
  4. Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

Explanation

Correct Answer

D:

The decisive detail is: A past loan decision dataset encodes inequitable approvals that a new system would reproduce. The appropriate business or technical decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly. The risk avoided is that biased historical records or unequal error rates can lead to harmful decisions even when overall accuracy looks high. Confirm the outcome through: An equity evaluation reports error and outcome metrics by relevant cohort with documented remediation and oversight.

Incorrect Answers

A:

This choice addresses prompt improvements before fine-tuning; its main associated risk is: Fine-tuning to compensate for missing current facts or poor requirements can bake outdated errors into a model. However, the situation A past loan decision dataset encodes inequitable approvals that a new system would reproduce is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

B:

This choice addresses human-in-the-loop for high-impact outputs; its main associated risk is: A generative system given unilateral decision authority can approve unsafe or inaccurate changes based on plausible text. However, the situation A past loan decision dataset encodes inequitable approvals that a new system would reproduce is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

C:

This choice addresses hallucination and unsupported claims; its main associated risk is: A plausible answer may confidently invent policies or product facts even when phrasing and grammar are polished. However, the situation A past loan decision dataset encodes inequitable approvals that a new system would reproduce is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

Question 544

A business case reports: A past loan decision dataset encodes inequitable approvals that a new system would reproduce. Why is this design likely to fail its intended business outcome?

  1. Biased historical records or unequal error rates can lead to harmful decisions even when overall accuracy looks high.
  2. A plausible answer may confidently invent policies or product facts even when phrasing and grammar are polished.
  3. Fine-tuning to compensate for missing current facts or poor requirements can bake outdated errors into a model.
  4. A generative system given unilateral decision authority can approve unsafe or inaccurate changes based on plausible text.

Explanation

Correct Answer

A:

This case exposes a specific failure mode: Biased historical records or unequal error rates can lead to harmful decisions even when overall accuracy looks high. In the situation A past loan decision dataset encodes inequitable approvals that a new system would reproduce, the recommended response is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly. Independent evidence would be: An equity evaluation reports error and outcome metrics by relevant cohort with documented remediation and oversight.

Incorrect Answers

B:

This choice addresses hallucination and unsupported claims; its main associated risk is: A plausible answer may confidently invent policies or product facts even when phrasing and grammar are polished. However, the situation A past loan decision dataset encodes inequitable approvals that a new system would reproduce is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

C:

This choice addresses prompt improvements before fine-tuning; its main associated risk is: Fine-tuning to compensate for missing current facts or poor requirements can bake outdated errors into a model. However, the situation A past loan decision dataset encodes inequitable approvals that a new system would reproduce is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

D:

This choice addresses human-in-the-loop for high-impact outputs; its main associated risk is: A generative system given unilateral decision authority can approve unsafe or inaccurate changes based on plausible text. However, the situation A past loan decision dataset encodes inequitable approvals that a new system would reproduce is primarily about bias, fairness and representativeness. The targeted decision is: Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.

Question 545

A business team evaluating generative AI encounters this situation: A procurement assistant has no access to the final signed contract and guesses its payment terms. It must choose a solution that is useful, secure, properly sourced, and appropriate for its constraints. Which response best meets the business need and its stated limits?

  1. Place qualified human review or explicit approval before consequential automated actions and use clear escalation when evidence is weak.
  2. Ground responses that depend on changing facts using an appropriate current trusted source instead of relying only on model training.
  3. Measure business-relevant correctness, retrieval quality, latency, cost and safety on representative held-out cases rather than only demonstrations.
  4. Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

Explanation

Correct Answer

D:

The decisive detail is: A procurement assistant has no access to the final signed contract and guesses its payment terms. The appropriate business or technical decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes. The risk avoided is that a strong model cannot reliably answer questions whose necessary evidence is missing, contradictory or inaccessible. Confirm the outcome through: A source-quality check traces a required answer to current approved records and reports gaps instead of inventing facts.

Incorrect Answers

A:

This choice addresses human-in-the-loop for high-impact outputs; its main associated risk is: A generative system given unilateral decision authority can approve unsafe or inaccurate changes based on plausible text. However, the situation A procurement assistant has no access to the final signed contract and guesses its payment terms is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

B:

This choice addresses knowledge cutoff and changing external facts; its main associated risk is: A model's training information may not reflect current prices, regulations, releases or world events. However, the situation A procurement assistant has no access to the final signed contract and guesses its payment terms is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

C:

This choice addresses evaluation metrics and representative data; its main associated risk is: Optimizing one easy metric can hide incorrect factual claims and failures on rare but important scenarios. However, the situation A procurement assistant has no access to the final signed contract and guesses its payment terms is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

Question 546

A gen AI initiative reports: A procurement assistant has no access to the final signed contract and guesses its payment terms. What independent evidence would best confirm the solution worked?

  1. A source-quality check traces a required answer to current approved records and reports gaps instead of inventing facts.
  2. A test with recently changed facts cites fresh dated evidence or explicitly states that reliable information is unavailable.
  3. A simulated high-impact case pauses with the evidence and only an authorized reviewer can approve its final action.
  4. A benchmark reports error types and meaningful KPI changes on normal, edge and adversarial cases with a baseline.

Explanation

Correct Answer

A:

The key acceptance evidence for A procurement assistant has no access to the final signed contract and guesses its payment terms is: A source-quality check traces a required answer to current approved records and reports gaps instead of inventing facts. It checks whether the team actually implemented: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes. A product configuration, demo, or persuasive response alone is not proof.

Incorrect Answers

B:

This choice addresses knowledge cutoff and changing external facts; its main associated risk is: A model's training information may not reflect current prices, regulations, releases or world events. However, the situation A procurement assistant has no access to the final signed contract and guesses its payment terms is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

C:

This choice addresses human-in-the-loop for high-impact outputs; its main associated risk is: A generative system given unilateral decision authority can approve unsafe or inaccurate changes based on plausible text. However, the situation A procurement assistant has no access to the final signed contract and guesses its payment terms is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

D:

This choice addresses evaluation metrics and representative data; its main associated risk is: Optimizing one easy metric can hide incorrect factual claims and failures on rare but important scenarios. However, the situation A procurement assistant has no access to the final signed contract and guesses its payment terms is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

Question 547

A business team evaluating generative AI encounters this situation: A project uses outdated FAQ pages as grounding even though newer policy documents exist. It must choose a solution that is useful, secure, properly sourced, and appropriate for its constraints. What should the initiative prioritize to address the primary issue?

  1. Assess source data and generated outcomes across relevant populations and use corrective governance when performance differs unfairly.
  2. Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.
  3. Treat ungrounded generated assertions as provisional; use reliable source evidence, validation and user-facing uncertainty for factual business answers.
  4. Track model, prompt, source-data and tool versions with monitoring for quality drift, security events and changed business outcomes.

Explanation

Correct Answer

B:

The decisive detail is: A project uses outdated FAQ pages as grounding even though newer policy documents exist. The appropriate business or technical decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes. The risk avoided is that a strong model cannot reliably answer questions whose necessary evidence is missing, contradictory or inaccessible. Confirm the outcome through: A source-quality check traces a required answer to current approved records and reports gaps instead of inventing facts.

Incorrect Answers

A:

This choice addresses bias, fairness and representativeness; its main associated risk is: Biased historical records or unequal error rates can lead to harmful decisions even when overall accuracy looks high. However, the situation A project uses outdated FAQ pages as grounding even though newer policy documents exist is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

C:

This choice addresses hallucination and unsupported claims; its main associated risk is: A plausible answer may confidently invent policies or product facts even when phrasing and grammar are polished. However, the situation A project uses outdated FAQ pages as grounding even though newer policy documents exist is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

D:

This choice addresses postdeployment monitoring and drift; its main associated risk is: A model that passed initial acceptance can deteriorate after source, traffic or product changes without visible errors. However, the situation A project uses outdated FAQ pages as grounding even though newer policy documents exist is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

Question 548

A business case reports: A project uses outdated FAQ pages as grounding even though newer policy documents exist. What is the strongest reason the current assumption is unsafe?

  1. A model that passed initial acceptance can deteriorate after source, traffic or product changes without visible errors.
  2. Biased historical records or unequal error rates can lead to harmful decisions even when overall accuracy looks high.
  3. A plausible answer may confidently invent policies or product facts even when phrasing and grammar are polished.
  4. A strong model cannot reliably answer questions whose necessary evidence is missing, contradictory or inaccessible.

Explanation

Correct Answer

D:

This case exposes a specific failure mode: A strong model cannot reliably answer questions whose necessary evidence is missing, contradictory or inaccessible. In the situation A project uses outdated FAQ pages as grounding even though newer policy documents exist, the recommended response is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes. Independent evidence would be: A source-quality check traces a required answer to current approved records and reports gaps instead of inventing facts.

Incorrect Answers

A:

This choice addresses postdeployment monitoring and drift; its main associated risk is: A model that passed initial acceptance can deteriorate after source, traffic or product changes without visible errors. However, the situation A project uses outdated FAQ pages as grounding even though newer policy documents exist is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

B:

This choice addresses bias, fairness and representativeness; its main associated risk is: Biased historical records or unequal error rates can lead to harmful decisions even when overall accuracy looks high. However, the situation A project uses outdated FAQ pages as grounding even though newer policy documents exist is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

C:

This choice addresses hallucination and unsupported claims; its main associated risk is: A plausible answer may confidently invent policies or product facts even when phrasing and grammar are polished. However, the situation A project uses outdated FAQ pages as grounding even though newer policy documents exist is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

Question 549

A business team evaluating generative AI encounters this situation: A dashboard includes duplicate records and an AI summary doubles the reported event count. It must choose a solution that is useful, secure, properly sourced, and appropriate for its constraints. Which approach is most defensible under these requirements?

  1. Place qualified human review or explicit approval before consequential automated actions and use clear escalation when evidence is weak.
  2. Measure business-relevant correctness, retrieval quality, latency, cost and safety on representative held-out cases rather than only demonstrations.
  3. Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.
  4. Treat ungrounded generated assertions as provisional; use reliable source evidence, validation and user-facing uncertainty for factual business answers.

Explanation

Correct Answer

C:

The decisive detail is: A dashboard includes duplicate records and an AI summary doubles the reported event count. The appropriate business or technical decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes. The risk avoided is that a strong model cannot reliably answer questions whose necessary evidence is missing, contradictory or inaccessible. Confirm the outcome through: A source-quality check traces a required answer to current approved records and reports gaps instead of inventing facts.

Incorrect Answers

A:

This choice addresses human-in-the-loop for high-impact outputs; its main associated risk is: A generative system given unilateral decision authority can approve unsafe or inaccurate changes based on plausible text. However, the situation A dashboard includes duplicate records and an AI summary doubles the reported event count is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

B:

This choice addresses evaluation metrics and representative data; its main associated risk is: Optimizing one easy metric can hide incorrect factual claims and failures on rare but important scenarios. However, the situation A dashboard includes duplicate records and an AI summary doubles the reported event count is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

D:

This choice addresses hallucination and unsupported claims; its main associated risk is: A plausible answer may confidently invent policies or product facts even when phrasing and grammar are polished. However, the situation A dashboard includes duplicate records and an AI summary doubles the reported event count is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

Question 550

The business case is: A dashboard includes duplicate records and an AI summary doubles the reported event count. Which TWO options together provide a suitable intervention and an independent acceptance check? (Select TWO.)

  1. Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.
  2. A source-quality check traces a required answer to current approved records and reports gaps instead of inventing facts.
  3. Measure business-relevant correctness, retrieval quality, latency, cost and safety on representative held-out cases rather than only demonstrations.
  4. A held-out factual test checks each mandatory claim against approved sources and flags unsupported responses.
  5. Place qualified human review or explicit approval before consequential automated actions and use clear escalation when evidence is weak.

Explanation

Correct Answers

A:

The intervention for the stated situation A dashboard includes duplicate records and an AI summary doubles the reported event count is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes. It directly mitigates: A strong model cannot reliably answer questions whose necessary evidence is missing, contradictory or inaccessible.

B:

A separate outcome check for the situation A dashboard includes duplicate records and an AI summary doubles the reported event count is: A source-quality check traces a required answer to current approved records and reports gaps instead of inventing facts. It measures the effect of the selected intervention rather than relying on the existence of a proposal.

Incorrect Answers

C:

This choice addresses evaluation metrics and representative data; its main associated risk is: Optimizing one easy metric can hide incorrect factual claims and failures on rare but important scenarios. However, the situation A dashboard includes duplicate records and an AI summary doubles the reported event count is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

D:

This choice addresses hallucination and unsupported claims; its main associated risk is: A plausible answer may confidently invent policies or product facts even when phrasing and grammar are polished. However, the situation A dashboard includes duplicate records and an AI summary doubles the reported event count is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.

E:

This choice addresses human-in-the-loop for high-impact outputs; its main associated risk is: A generative system given unilateral decision authority can approve unsafe or inaccurate changes based on plausible text. However, the situation A dashboard includes duplicate records and an AI summary doubles the reported event count is primarily about data-dependency and poor-source limitations. The targeted decision is: Improve source data completeness, consistency and permission-aware retrieval before investing in aggressive prompt changes.