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Improve instructions, examples and context through prompt engineering first; consider parameter-efficient tuning only when the model and business case justify trained adaptations. This step is associated with Prompt engineering versus prompt tuning, but it is not one of the two complementary requirements here. The intended response and verification are: Use multimodal foundation models when combined image, text or other supported inputs materially improve task understanding, and validate each modality's interpretation. Plus: Inspect source attachments, supported formats, image resolution, text-image alignment, failure rates and human verification. Scenario: A property insurer combines a photo of damage with the claimant's written description.

Check input/output modality support, file and duration limits, evaluation examples, integration constraints and privacy requirements. This step is associated with Select foundation models by task modality, but it is not one of the two complementary requirements here. The intended response and verification are: Use multimodal foundation models when combined image, text or other supported inputs materially improve task understanding, and validate each modality's interpretation. Plus: Inspect source attachments, supported formats, image resolution, text-image alignment, failure rates and human verification. Scenario: A property insurer combines a photo of damage with the claimant's written description.

The chosen settings meet creativity or consistency requirements in tests while unsupported combinations fail clearly. This step is associated with Temperature and top-p sampling behavior, but it is not one of the two complementary requirements here. The intended response and verification are: Use multimodal foundation models when combined image, text or other supported inputs materially improve task understanding, and validate each modality's interpretation. Plus: Inspect source attachments, supported formats, image resolution, text-image alignment, failure rates and human verification. Scenario: A property insurer combines a photo of damage with the claimant's written description.

Inspect disclosure policy, model use records, citations, decision owner, review controls, appeals and incident handling. These observations are relevant to Explainability accountability and transparent AI decisions, but do not isolate the main question. The decisive evidence is: Inspect affected roles, approval responsibilities, training coverage, user feedback, escalation path and business continuity plan. Scenario: A customer support rollout changes agent duties without informing managers or defining exceptions.

Review pre-pilot measurements, cohort size, rework rate, human escalation, operating cost and decision thresholds. These observations are relevant to Pilot KPIs and return on investment, but do not isolate the main question. The decisive evidence is: Inspect affected roles, approval responsibilities, training coverage, user feedback, escalation path and business continuity plan. Scenario: A customer support rollout changes agent duties without informing managers or defining exceptions.

Inspect publication dates, source-of-record updates, retrieval index refresh, permissions and citations. These observations are relevant to Knowledge cutoff and fresh business information, but do not isolate the main question. The decisive evidence is: Inspect affected roles, approval responsibilities, training coverage, user feedback, escalation path and business continuity plan. Scenario: A customer support rollout changes agent duties without informing managers or defining exceptions.

Check service channels, conversation topics, permitted transaction tools, language coverage and human fallback availability. These observations are relevant to Conversational Agents for self-service customer support, but do not isolate the main question. The decisive evidence is: Inspect source contracts, chunk boundaries, embedding/index freshness, permission filters and evaluation of retrieved snippets. Scenario: A legal workflow needs custom per-client evidence partitions and a controlled search pipeline.

Inspect requested media type, prompt constraints, example outputs, brand restrictions and rights review before publishing. These observations are relevant to Diffusion based visual generation, but do not isolate the main question. The decisive evidence is: Inspect source contracts, chunk boundaries, embedding/index freshness, permission filters and evaluation of retrieved snippets. Scenario: A legal workflow needs custom per-client evidence partitions and a controlled search pipeline.

Inspect human agent workflow, approved knowledge sources, interaction recording policy, recommendation quality and escalations. These observations are relevant to Agent Assist support for human contact-center agents, but do not isolate the main question. The decisive evidence is: Inspect source contracts, chunk boundaries, embedding/index freshness, permission filters and evaluation of retrieved snippets. Scenario: A legal workflow needs custom per-client evidence partitions and a controlled search pipeline.

Break complex tasks into bounded stages with validated intermediate outputs, using prompt chaining only where sequential dependencies are real. This step is associated with Prompt chaining and staged transformations, but it is not one of the two complementary requirements here. The intended response and verification are: Use clear task instructions first and add one or a few representative input-output examples when ambiguity warrants them, testing on unseen cases. Plus: Inspect prompt task statement, example diversity, expected response format, edge cases and token cost. Scenario: A marketing analyst adds twenty nearly identical examples and assumes the model cannot overfit the demonstrated pattern.

Known performance gaps are documented and reduced or escalated before consequential use. This step is associated with Fairness evaluation across subgroups, but it is not one of the two complementary requirements here. The intended response and verification are: Use clear task instructions first and add one or a few representative input-output examples when ambiguity warrants them, testing on unseen cases. Plus: Inspect prompt task statement, example diversity, expected response format, edge cases and token cost. Scenario: A marketing analyst adds twenty nearly identical examples and assumes the model cannot overfit the demonstrated pattern.

Inspect dataset representation, disaggregated performance, adverse outcomes, reviewer criteria and monitored drift. This step is associated with Bias fairness and edge case evaluation, but it is not one of the two complementary requirements here. The intended response and verification are: Use clear task instructions first and add one or a few representative input-output examples when ambiguity warrants them, testing on unseen cases. Plus: Inspect prompt task statement, example diversity, expected response format, edge cases and token cost. Scenario: A marketing analyst adds twenty nearly identical examples and assumes the model cannot overfit the demonstrated pattern.

Monitor deployed generative AI with reproducible evaluations, task KPIs, version records and drift or incident tracking, not only subjective user praise. This response is aimed at Model monitoring versioning and quality KPIs, not the controlling requirement here. Foundation-model updates and data drift can change outcomes; continuous monitoring must include both technical and business indicators. The targeted response is: Address model knowledge-cutoff limitations with authorized current retrieval and source freshness validation rather than assuming training contains the latest facts. Scenario: A human resources team expects newly approved procedures to be known without indexing or supplying them.

Ground responses in authorized first-party business content when the question concerns internal policy, using retrieval with access and freshness checks. This response is aimed at First-party enterprise grounding and source access, not the controlling requirement here. First-party grounding uses an organization's own data; public web facts cannot supersede confidential internal rules. The targeted response is: Address model knowledge-cutoff limitations with authorized current retrieval and source freshness validation rather than assuming training contains the latest facts. Scenario: A human resources team expects newly approved procedures to be known without indexing or supplying them.

Use image-generation models employing diffusion concepts for creating or editing visual content, with separate fidelity and rights checks. This response is aimed at Diffusion based visual generation, not the controlling requirement here. Diffusion refers to a generative modeling approach common in imagery, not a guarantee of factual provenance or an editing audit trail. The targeted response is: Address model knowledge-cutoff limitations with authorized current retrieval and source freshness validation rather than assuming training contains the latest facts. Scenario: A human resources team expects newly approved procedures to be known without indexing or supplying them.

Inspect data contracts, cleaning provenance, validation evidence, deployment approvals, model versions and post-launch monitoring. This step is associated with ML lifecycle from ingestion to management, but it is not one of the two complementary requirements here. The intended response and verification are: Choose Imagen for still-image creation and editing and Veo when the requested artifact is generated video, subject to current feature availability. Plus: Inspect the requested media output, duration or resolution, brand policy, availability and sample fidelity. Scenario: A product manager selects Veo expecting its primary output to be a relational SQL report.

The model meets agreed fairness and error thresholds across supported groups and escalates poorly represented cases. This step is associated with Bias fairness and edge case evaluation, but it is not one of the two complementary requirements here. The intended response and verification are: Choose Imagen for still-image creation and editing and Veo when the requested artifact is generated video, subject to current feature availability. Plus: Inspect the requested media output, duration or resolution, brand policy, availability and sample fidelity. Scenario: A product manager selects Veo expecting its primary output to be a relational SQL report.

Separate infrastructure, models, platforms, agents and applications by their roles, mapping each requirement to the layer that owns it. This step is associated with Five-layer gen AI landscape taxonomy, but it is not one of the two complementary requirements here. The intended response and verification are: Choose Imagen for still-image creation and editing and Veo when the requested artifact is generated video, subject to current feature availability. Plus: Inspect the requested media output, duration or resolution, brand policy, availability and sample fidelity. Scenario: A product manager selects Veo expecting its primary output to be a relational SQL report.

Inspect ground-truth definitions, annotator agreement, label noise, class distribution and representative unlabeled sources. This step is associated with Labeled versus unlabeled source governance, but it is not one of the two complementary requirements here. The intended response and verification are: Choose Imagen for still-image creation and editing and Veo when the requested artifact is generated video, subject to current feature availability. Plus: Inspect the requested media output, duration or resolution, brand policy, availability and sample fidelity. Scenario: A designer wants high-quality still concept renders to compare packaging before a launch.

Use reinforcement learning concepts for policies refined through rewards and sequential interaction, not as a synonym for ordinary supervised labeled prediction. This step is associated with Reinforcement learning and feedback signals, but it is not one of the two complementary requirements here. The intended response and verification are: Choose Imagen for still-image creation and editing and Veo when the requested artifact is generated video, subject to current feature availability. Plus: Inspect the requested media output, duration or resolution, brand policy, availability and sample fidelity. Scenario: A designer wants high-quality still concept renders to compare packaging before a launch.

The user receives useful assistance inside permitted Workspace content and reviews the draft before external distribution. This step is associated with Gemini for Google Workspace productivity, but it is not one of the two complementary requirements here. The intended response and verification are: Choose Imagen for still-image creation and editing and Veo when the requested artifact is generated video, subject to current feature availability. Plus: Inspect the requested media output, duration or resolution, brand policy, availability and sample fidelity. Scenario: A designer wants high-quality still concept renders to compare packaging before a launch.

Sequence a model initiative through data ingestion, preparation, training, deployment and ongoing management with separate gates and accountable owners. This step is associated with ML lifecycle from ingestion to management, but it is not one of the two complementary requirements here. The intended response and verification are: Choose a foundation model whose supported modalities match the required inputs and outputs rather than assuming any text-only model handles video or audio natively. Plus: Check input/output modality support, file and duration limits, evaluation examples, integration constraints and privacy requirements. Scenario: A media company needs to generate short promotional video clips rather than still images.

Review UI obligations, action permissions, model lifecycle, managed platform capabilities and workflow accountability. This step is associated with Applications versus agents and platforms, but it is not one of the two complementary requirements here. The intended response and verification are: Choose a foundation model whose supported modalities match the required inputs and outputs rather than assuming any text-only model handles video or audio natively. Plus: Check input/output modality support, file and duration limits, evaluation examples, integration constraints and privacy requirements. Scenario: A media company needs to generate short promotional video clips rather than still images.

Answers refer to the correct approved enterprise source or declare insufficient evidence while enforcing permissions. This step is associated with First-party enterprise grounding and source access, but it is not one of the two complementary requirements here. The intended response and verification are: Choose a foundation model whose supported modalities match the required inputs and outputs rather than assuming any text-only model handles video or audio natively. Plus: Check input/output modality support, file and duration limits, evaluation examples, integration constraints and privacy requirements. Scenario: A media company needs to generate short promotional video clips rather than still images.

The selected infrastructure supports the required AI service with observed capacity and cost without assuming a TPU is an application. This result assesses AI-optimized infrastructure accelerators and capacity, not the business outcome under test. The relevant acceptance condition is: The AI service maintains layered controls against representative misuse, with findings assigned and mitigated. Scenario: A buyer assumes adopting an AI security framework means audit logging and permission checks happen automatically.

Staff use the new workflow appropriately and retain control of consequential actions with measurable adoption quality. This result assesses Organizational adoption and human workflow redesign, not the business outcome under test. The relevant acceptance condition is: The AI service maintains layered controls against representative misuse, with findings assigned and mitigated. Scenario: A buyer assumes adopting an AI security framework means audit logging and permission checks happen automatically.

Each release stage meets budget and quality gates with documented owner approval before larger expansion. This result assesses Total cost and staged solution integration, not the business outcome under test. The relevant acceptance condition is: The AI service maintains layered controls against representative misuse, with findings assigned and mitigated. Scenario: A buyer assumes adopting an AI security framework means audit logging and permission checks happen automatically.

The solution creates useful draft content only where permitted, while fixed-category decisions remain measurable and testable. This result assesses Distinguish generative AI from classification AI, not the business outcome under test. The relevant acceptance condition is: The chosen model processes required documents within budget and quality constraints and fails clearly on oversized requests. Scenario: A travel app chooses the most costly model for short simple greetings and then misses its unit economics target.

Answers accurately cite existing approved information or acknowledge missing support without fabricating details. This result assesses Hallucination detection and source verification, not the business outcome under test. The relevant acceptance condition is: The chosen model processes required documents within budget and quality constraints and fails clearly on oversized requests. Scenario: A travel app chooses the most costly model for short simple greetings and then misses its unit economics target.

Summaries cite allowed sources, generated assets meet brand rules, and state-changing tasks require verified authorization. This result assesses Summarize discover create and automate use cases, not the business outcome under test. The relevant acceptance condition is: The chosen model processes required documents within budget and quality constraints and fails clearly on oversized requests. Scenario: A travel app chooses the most costly model for short simple greetings and then misses its unit economics target.

Choose Speech-to-Text, Text-to-Speech, Translation and related APIs for specialized audio and language operations, validating quality and customer context. This response is aimed at Speech translation and language tool services, not the controlling requirement here. Specialized AI APIs address distinct input and output tasks; an unverified transcript can corrupt later generated summaries. The targeted response is: Consider supported Agent Platform AutoML for fitting a task-specific prediction model from data where a foundation model is not necessary or insufficient. Scenario: An organization has no approved training targets yet assumes automated training can produce reliable classification labels.

Use Agent Search prebuilt retrieval-augmented generation when answers should be grounded in approved indexed enterprise content without building every retrieval component. This response is aimed at Agent Search prebuilt RAG for enterprise knowledge, not the controlling requirement here. Prebuilt RAG combines retrieval and generation; it does not turn stale or unauthorized documents into trustworthy facts. The targeted response is: Consider supported Agent Platform AutoML for fitting a task-specific prediction model from data where a foundation model is not necessary or insufficient. Scenario: An organization has no approved training targets yet assumes automated training can produce reliable classification labels.

Use Grounding with Google Search for relevant current publicly accessible information, distinct from private enterprise retrieval. This response is aimed at Grounding with Google Search for public current facts, not the controlling requirement here. Public-world grounding can improve freshness but does not validate proprietary records or every search result's accuracy. The targeted response is: Consider supported Agent Platform AutoML for fitting a task-specific prediction model from data where a foundation model is not necessary or insufficient. Scenario: An organization has no approved training targets yet assumes automated training can produce reliable classification labels.