Chat Prompt Templates for Strategy Briefs and Research

Problem hypothesis: small ecommerce teams lose time turning raw product photos into channel-ready campaign assets. Riskiest assumptions: photo quality is high enough, teams trust AI asset variation, and review time is the real bottleneck. Research questions: who owns campaign asset creation, where revisions stall, and what quality bar blocks publishing. Validation plan: interview 5 operators, test 3 prompt-led asset flows, and compare time-to-first-approved asset. Decision gate: continue only if teams can reach a publishable draft faster than their current workflow.

Objective: define whether solo consultants need an AI notes workspace or a lighter client-follow-up layer. Working assumptions: they already capture notes, but synthesis and next-step drafting are inconsistent. Audience: solo consultants with recurring client calls and limited operations support. Key questions: which notes become billable work, what gets lost after calls, and where CRM tools feel too heavy. Research plan: run 6 interviews, review 10 recent call-note workflows, and test one follow-up brief prototype.

Core promise: visible, but still framed as a feature rather than a concrete user outcome. Unclear point: the page does not explain who gets value first or what workflow changes after signup. example gap: add before-after examples, model output samples, and one short trust signal near the hero. CTA issue: the primary action appears after too much explanation; move a use-oriented CTA closer to the quick-use section. Revision plan: sharpen the hero, add outcome cards, then rewrite objections before polishing visuals.

Issue type: repeated export failure plus refund request. Customer-facing reply: acknowledge the failed attempts, apologize plainly, and confirm you will help recover the export path first. Policy boundary: explain refund eligibility only from the provided policy notes; do not promise an exception. Next step: ask for export format, browser, and timestamp, then route to billing if the account meets refund criteria. Internal note: tag as product reliability risk because the same export failed twice.

Objective: drive qualified trial starts from solo consultants. Audience: independent consultants managing scattered client notes. Core message: fewer dropped follow-ups, less admin drag. Channels: LinkedIn posts, founder email, comparison landing page, and retargeting. Next actions: define offer, collect example points, draft three creative angles.

Category frame: meeting capture plus follow-up automation. Pattern: incumbents compete on transcription accuracy and integrations. Gap: small agencies need client-ready summaries and action ownership. Risk: privacy concerns can block adoption. Opportunity: position around client handoff quality, not generic notes.

Research goal: understand how feedback becomes prioritized work. Participant profile: freelance designers with active client projects. Warm-up: ask about recent project flow. Core questions: where feedback arrives, how it is triaged, what gets lost. Bias check: avoid asking whether they want our proposed feature.

Decisions: keep the starter tier, test annual discount messaging, delay enterprise packaging. Actions: Maya drafts pricing FAQ; Jordan pulls churn data; Priya reviews checkout copy. Open questions: final discount amount and launch date. Follow-up: concise recap with owners and unknowns clearly marked.

Headline: activation improved, but early value may not be sticking. What changed: more users complete onboarding; fewer return in week two. Likely drivers: faster first success but weaker follow-up loop. Action: inspect post-onboarding prompts and segment by acquisition channel. Watchout: do not treat this as causality yet.

Problem: users lose repeatable prompts after discovery. Goal: save and reopen favorite prompt templates. Non-goals: folders, team sharing, ranking, and custom prompt marketplace. Requirements: favorite button, saved list, empty state, analytics events. Open questions: limits, auth state, and mobile placement.

Finding: webhook retries may create duplicate credits if the idempotency key is not enforced. Risk: billing state can diverge from the user-visible wallet. Test gap: add replay and out-of-order event cases. Decision: block merge until persistence and retry behavior are covered.

Known signal: the filter state disappears during navigation, not initial load. Likely surfaces: query hydration, locale routing, and client state reset. Reproduction path: open list, select model, enter detail, return with browser back. Evidence to collect: URL, input value, console errors, and network cache behavior.

First split: credentials, environment variables, and provider project scope. Request check: compare auth header shape in draft and production. Provider check: confirm the production key has audio generation enabled. Next step: log redacted request metadata and test a minimal production request.

Purpose: count users who opened or used a prompt template in the selected window. Join risk: events may duplicate users unless the query deduplicates by user id. Filter risk: locale and anonymous sessions may change the denominator. Performance: index event_name and created_at before running this on full history.

Audience: maintainers replacing draft public files with approved media URLs. Outline: asset contract, upload path, metadata fields, validation commands, rollback notes. Missing context: exact managed storage bucket policy and cache invalidation behavior. Next step: add one worked example for image, video, audio, and chat assets.

Headline: Prompt templates now show clearer examples by type. User value: chat, audio, image, and video templates are easier to scan before starting. Operational note: final asset storage review remains a separate launch item. Follow-up: video templates still need a playable video example.

Email 1: choose a model before spending credits. Email 2: start from a prompt template instead of a blank page. Email 3: review outputs and save repeatable workflows in Studio. CTA pattern: each email should drive one concrete action.

Week 1: educate users on choosing models and adapting prompt templates. Week 2: show examples across image, audio, video, and chat. Cadence: three short posts, one guide, one comparison thread per week. Measurement: template clicks, model starts, and saved workflows.

Intent: users want reusable prompts and examples before generating audio. Angle: focus on voiceover, dialogue, sound effects, and cleanup workflows. Sections: model choice, prompt anatomy, example expectations, and managed media notes. Internal links: audio models, prompt gallery, and Studio workflow pages.

Best current hook: the one naming tab switching and repeated setup. Weakest hook: too broad, sounds like generic AI productivity. Next test: contrast one-wallet workflow against separate tool subscriptions. Keep: concrete action words like choose, run, review, save.

Voice principles: practical, evidence-led, calm, and specific. Use: workflow, model choice, example, review, saved context. Avoid: supernatural promises, zero-work phrasing, category-changing hype, uncapped claims, and guarantee-style claims. Example rewrite: replace hype with a concrete before-and-after workflow claim.

Public reply: explain that outputs can be used commercially when the user has rights in inputs and follows provider terms. Tone: helpful, not defensive. Avoid: blanket legal guarantees. Next step: link to usage and terms guidance.

Opening: reference their practical AI workflow audience. Mutual value: template examples give readers a starting point, not just model news. Offer: share a curated audio and chat prompt pack. CTA: ask if a short resource mention fits their upcoming issue.

Summary: the prompt library moved from 40 templates toward a 200-template target. Evidence: audio and chat categories now have stronger example coverage. Risk: image and video still need final media review and managed storage migration. Ask: feedback on which workflows should be prioritized for distribution.

Objection type: switching cost and budget fatigue. Reply angle: Rivya is not another single-purpose tool; it consolidates discovery, prompts, outputs, and credits. example to show: one workflow moving from prompt template to result review. Do not claim: automatic cost savings without their usage data.

Theme 1: users choose by examples before reading model specs. Evidence: several participants asked for example clips and prompt starters. Implication: model pages should surface related prompt templates earlier. Open question: whether users trust draft example before final managed media.

Cluster A: users want examples that show output shape before running. Cluster B: users need model recommendations explained in plain language. Cluster C: users worry about media rights and final example quality. Action: add example status labels and clearer model-fit notes.

Persona 1: solo creator comparing models before spending credits. Scenario: starts from image prompts, then needs audio copy for a reel. Pain: separate tools break context and budget clarity. Design implication: keep prompt, model, result, and credit context visible together.

Value metric: templates increase first-run confidence before credits are spent. Free argument: discovery content reduces blank-page friction. Paid argument: saved custom workflows may belong in account features. Risk: hiding templates too early weakens SEO and activation.

Reach: template expansion touches more pages, but video example has higher trust impact. Impact: video example resolves the clearest expectation mismatch. Confidence: audio/chat expansion is easier to execute reliably. Recommendation: finish audio/chat scale, then prioritize video example before more public promotion.

Option A: expand prompts increases library depth and SEO surface. Option B: final media governance increases trust and reduces launch risk. Decision logic: complete audio/chat scale only if quality gates stay automated. Next gate: image/video example must not be skipped before launch positioning.

Decision: keep small category batches until example checks are stable. Owner: content lead drafts templates; engineering verifies resource paths. Action: add audio duration audit to the prompt checklist. Follow-up: review video example risk before expanding public promotion.

Case 1: list page renders 50 audio cards without overflow. Case 2: each detail page exposes audio controls and full prompt. Case 3: every audioUrl resolves to a readable local file. Case 4: model filter still works with expanded template count.

Impact: four audio templates showed controls but had unreadable m4a files. Root cause: generation script wrote placeholder files without audio validation. Detection gap: prompts check validated fields but not media readability. Action: add afinfo-based audit before marking audio draft complete.

Risk: the claim overstates rights and may encourage misuse of third-party media. Safer framing: users must have rights in prompts, uploads, and source materials. Product note: draft example can be replaced before final final publication. Recommendation: avoid blanket permission language.

Clear point: explain what is stored for the product to function. Trust point: state that third-party providers process generation and chat requests. Risk: avoid saying no data ever leaves Rivya if providers are involved. Rewrite direction: concrete, plain, and linked to policy details.

Plain summary: identify who can use generated outputs and under what conditions. Business risk: note any restrictions tied to inputs, provider policy, or prohibited use. Unknowns: mark anything requiring legal review. Boundary: do not present this as legal advice.

Core competency: workflow thinking across image, video, audio, and chat. Evidence: can create complete templates with example and localization. Interview task: audit one template for missing media and weak prompt structure. Rubric: score specificity, quality bar, and operational judgment.

Strength: high output speed and willingness to take on messy content work. Gap: media example validation is inconsistent and creates rework. Example: unreadable audio files reached draft before afinfo checks. Next step: use the checklist before marking any category complete.

Module 1: understand the four prompt types and example requirements. Module 2: write complete prompts with model fit and output shape. Module 3: create draft media and run validation commands. Assessment: audit one weak template and fix it end to end.

Observed variance: more templates can create more first-run tests. Likely drivers: audio example checks, model comparison, and repeated page QA. Watchout: separate organic user usage from internal governance runs. Next data: segment by user type, model, and source page.

Main assumption: content generation scales without example quality dropping. Constraint: audio and video examples require more validation than chat. Missing input: average time per media asset and managed storage migration capacity. Decision point: expand only after category-level audits pass.

Hypothesis: clearer example labels increase template use starts. Variant: add example status and model-fit notes near the CTA. Metric: prompt template use click-through and detail page scroll depth. Guardrail: no drop in audio play interaction or page speed.

Readout: template use increased, but users may be skipping example playback. Possible explanation: CTA is clearer, while audio example feels secondary. Risk: more starts without example review may reduce output satisfaction. Next test: keep CTA clarity and make audio example status more visible.

Diagnosis: "pleasant" is subjective and does not specify event, duration, or avoid list. Rewrite: create a 2-second low-distraction success cue for saved output confirmation. Add constraints: soft attack, short tail, no alarm, no melody. Next step: generate one example and compare it with the UI moment.

Task type: voice narration with natural delivery and multilingual options. Recommended start: ElevenLabs Multilingual for quality and language flexibility. Faster alternative: ElevenLabs Turbo if iteration speed matters more. Prompt note: include duration, voice direction, script structure, and what the narration should avoid.

Image: key visual showing prompt categories and example states. Video: short walkthrough from template list to detail example. Audio: calm narration plus UI confirmation cues. Chat: campaign brief and support reply templates for launch operations.

Subject: a polished prompt template card in a practical AI workspace. Layout: clean product UI, visible model badge, output preview, and CTA. Style: modern editorial product shot, not abstract AI art. Avoid: fake text blocks, illegible UI, and one-note purple glow.

Opening risk: the first line explains the library before showing a workflow problem. example gap: add one visible template-to-result transition by second six. Pacing: keep one idea per shot and avoid feature list narration. Revision: start with scattered tools, then reveal the Rivya prompt path.

Use case: generation finished and the result is ready for review. Tone: calm confirmation, not alarm or celebration. Sound design: soft two-note cue with a short airy tail. Avoid: harsh bells, voice, long melody, or anything that masks narration.

Decision: prioritize prompt example coverage first. Context: model pages now consume prompt-derived examples, while legacy examples remain inventory. Options: clean old samples now, add prompt coverage now, or split the sprint. Recommendation: add prompt coverage for uncovered models, then clean old compatibility data in a later pass. Risk: temporary media URLs still block final media governance. Next milestone: every chat and audio model has at least one published prompt example.

Research question: what blocks teams from using AI media examples in public pages? Evidence: storage ownership, rights review, and repeatable approval paths came up most often. Buyer constraint: teams need auditability before speed. Contradiction: they want faster output, but do not trust unmanaged links. Confidence: medium; five interviews show a pattern, not a market example. Next research: test whether reviewed template examples reduce maintenance work.

Severity: medium. Category: audio playback / media asset. Likely cause: file exists but browser cannot decode it, or the URL points to draft example that was not regenerated. Missing evidence: browser console, network status, content-type, and afinfo result. First reply: ask for the URL, browser, and timestamp while confirming we are checking the media asset. Escalate if multiple templates share the same silent file.

Thanks for asking. We are treating reviewed examples as a launch gate, not a cosmetic task. The current plan is to keep draft assets separate, move final examples to approved URLs, and document any compatibility behavior that remains. I cannot frame that as a blanket guarantee until the final audit passes. Next step: I can share the current audit status and remaining replacement list.

Executive summary: draft assets support iteration, but cannot be treated as final launch material. Risk: customers may see placeholder-like previews, source ownership may be unclear, and the search image strategy may stay deferred. Controls: asset audit, content ownership checks, and manual page sampling. Decision needed: approve a launch gate that separates content coverage from final asset readiness. Owner: content governance and product marketing owners jointly.

Core thesis: more templates improve example coverage and SEO surface area. Weak assumption: every model deserves equal template depth. Failure mode: thin pages dilute quality and increase maintenance load. Second-order effect: users may trust model pages less if examples feel repetitive. Safer alternative: require one high-quality Prompt example for every model, then six only for strategic or high-traffic models. Next test: measure model page engagement before expanding the long tail.

Goal: make reviewed content records the source of truth for examples. Current architecture: pages still read a mix of inline examples and derived UI props. Target architecture: server code reads published records by content type and keeps compatibility only during migration. Steps: add an aggregation layer, update public pages, update audits, document compatibility behavior, then remove legacy fields after coverage. Tests: content check, media audit, model content audit, typecheck, and page sampling.

Change summary: the home page now derives its featured example on the server and passes it into client blocks. Behavior impact: Hero, Features, and Gallery receive the same reviewed example without client components importing server-only modules. Why this approach: it preserves static rendering and keeps ownership boundaries clear. Verification: typecheck should confirm the prop contracts. Residual risk: page sampling is still needed to confirm the example rail looks right on mobile.

Risk areas: duplicate slugs, wrong recommended model category, missing locale fields, invalid audio files, and list page density. Automated checks: prompts check, i18n generate/check, media example audit, and typecheck. Manual checks: sample one chat detail page and one audio detail page in en and zh. Negative cases: missing audioUrl, missing conversation example, and model/category mismatch. Stop condition: any published template fails schema or audio cannot be read.

Goal: route launch examples through reviewed prompt templates. Required changes: add example aggregation, update pages, update audits and docs. Out of scope: changing provider config, billing params, runtime forms, or prompt database storage. Compatibility: keep the old path only until reviewed coverage is complete. Acceptance: all launch example surfaces prefer reviewed prompt examples and typecheck passes.

Scope: prompt coverage and media governance copy. Blockers: none if prompts check, media audit, typecheck, and build pass. Warnings: temporary media remains until final approved storage replacement. Required checks: prompts check, media example audit, content models audit, typecheck, build, and page sampling. Owner decision: confirm final media storage before launch.

Executive summary: the launch case is directionally positive, but the pricing signal is not yet strong enough for a full rollout. Evidence: survey comments point to clear workflow pain, the pricing notes show willingness to pay in two segments, and the dashboard screenshot suggests activation is strongest after the second saved project. Risks: the sample over-represents power users, and the screenshot does not prove retention. Next steps: run a smaller paid pilot, compare activation by segment, and define the go/no-go threshold before expanding.

Recommendation: proceed with a limited rollout instead of a full launch. Why now: the workflow solves a visible customer pain, and the current support load gives us enough real cases to test against. Tradeoff: a narrow rollout slows top-line launch momentum, but it gives the team cleaner evidence on activation, support burden, and pricing sensitivity. Risks: unclear ownership for post-launch review and incomplete failure-mode documentation. Decision needed: approve the pilot scope, success thresholds, and review date before implementation begins.

Target segment: teams that already track funnel data but need faster product-level interpretation. Customer problem: they have dashboards, but the notes show friction in translating metrics into decisions. Positioning options: workflow intelligence, launch review companion, or lightweight product analytics layer. Evidence strength: buyer pain is supported by interviews; pricing is directional; channel fit is still weak. Recommended entry path: start with a focused add-on for existing users, validate paid usage in two segments, and avoid a broad category claim until retention data is clearer.

Highest risk: data integrity during the backfill, because the plan describes the write path but does not define an idempotency check. Secondary risk: rollback is described operationally, but the schema downgrade path is unclear. Test gaps: no fixture covers partially migrated records, duplicate replay, or empty optional fields. Monitoring needs: add counters for migrated, skipped, retried, and failed records, plus a post-run consistency query. Smallest mitigation: add an idempotent migration marker, run a dry-run sample, and define the exact stop condition before production.

Theme 1: users want faster triage, not another dashboard. Evidence appears in repeated comments about deciding what to fix first. Theme 2: trust depends on traceability. Several notes ask where a recommendation came from. Objection: buyers worry the workflow will add review overhead. Messaging language: emphasize a short decision path, visible evidence, and fewer manual status meetings. Follow-ups: interview low-frequency users, test the traceability copy, and confirm whether triage speed affects renewal intent.

Headline: growth quality improved, but the next quarter depends on retention and sales focus. Business context: activation is improving after onboarding changes, while expansion revenue remains concentrated in a narrow segment. Key metrics: include only the numbers from the notes and label any missing trend data. Risks: customer concentration, unclear payback period for the new channel, and unresolved support load. Board asks: approve the next-quarter focus, confirm risk tolerance for the channel test, and align on the retention threshold for expansion.

What changed: the update appears to tighten the requirements for how user-generated outputs are reviewed and disclosed. Affected workflows: creation, moderation review, public sharing, and help-center copy. User impact: users may need clearer disclosure and fewer ambiguous publishing states. Escalations: confirm the exact compliance interpretation with counsel before changing public terms. Owner checklist: audit public copy, map affected flows, define the review threshold, update internal playbooks, and schedule a follow-up review before the effective date.

Recommendation: prioritize the guided review workflow before expanding the automation layer. Context: users already understand the core value, but the notes show friction when they have to judge output quality manually. Tradeoff: this delays a more ambitious automation promise, but it improves trust and makes future automation easier to evaluate. Risks: unclear success metrics and possible onboarding complexity. Decision requested: approve guided review as the next milestone and confirm the metric used to judge whether it is working.

Summary: the outage affected new project creation for a limited window while existing sessions stayed available. Customer impact: users could view saved work, but some could not start new generation tasks. Contributing factors: the notes point to a missing retry limit, unclear alert ownership, and a deployment check that did not cover the affected path. What worked: rollback was fast once the owner was identified. Action items: add the missing check, define alert ownership, test retry limits, and schedule a follow-up review with due dates.

Opening: this month showed stronger product usage and clearer sales focus, while retention work remains the main operating priority. Wins: onboarding changes improved activation, and two customer conversations validated the core workflow. Metrics: include only the provided figures and label missing retention trend data. Risks: expansion is still concentrated, and support load may rise with the next feature. Asks: introductions to design partners in the target segment and feedback on the pricing package before the next pilot.

Role context: senior product designer for a workflow-heavy product. Must-have criteria: systems thinking, user research depth, cross-functional communication, and shipping judgment. Strengths: notes show strong research synthesis and clear design rationale. Concerns: limited evidence on engineering collaboration and prioritization under constraints. Missing signals: no example of resolving disagreement with product or engineering. Recommendation: continue to final panel, with follow-up focused on tradeoffs, implementation partnership, and how the candidate measures design impact.

Audience: existing teams that already use the workspace for repeated creative reviews. Product change: the new workflow helps them compare outputs, keep notes, and decide what to revise next. Value proposition: fewer scattered reviews and a clearer path from draft to approved asset. Proof points: use only the provided activation and customer feedback notes. Positioning boundaries: avoid claiming full automation, guaranteed quality, or replacement of human review. Review questions: confirm the success metric, support readiness, and which claims can appear on public pages.

Board risk: demand is promising but unit economics are not ready. Likely question: what support capacity breaks first. Prep answer: show demand signals separately from margin assumptions. Decision needed: approve research budget, not full rollout. Owner follow-up: finance model due before the next review.

Confirmed decision: no confirmed launch date yet. Open question: can onboarding copy be approved before Friday. Owner: product owns copy approval; support owns help doc draft. Risk: marketing date can slip if launch dependencies stay unresolved. Next check: 24-hour decision point on copy and help doc readiness.

Base case: team workspaces help agency and studio accounts without changing the solo flow. Upside: collaboration creates expansion revenue and lowers churn. Downside: permissions and billing complexity slow the core roadmap. Early signal: ask sales to tag qualified team requests for two weeks. Reversible decision: prototype invite and shared history before full admin roles.

Capacity risk: launch design is the bottleneck across three workstreams. Dependency risk: pricing test copy may depend on help center wording. Unclear owner: no owner named for launch sequencing. Decision needed: choose one primary launch or assign backup design support. Watch metric: missed design review dates by workstream.

Likely question: will credit packs reduce predictable revenue. Why it matters: finance needs forecast confidence. Answer outline: show current contract friction, expected conversion lift, and retention risk. Evidence needed: renewal behavior by segment. Answer to avoid: claiming churn will improve without cohort data.

Strong evidence: agencies are asking sales about multi-seat use. Weak evidence: interview demand may come from a narrow sample. Contradiction: analytics show mostly solo behavior today. Assumption: shared history will create enough value before permissions are complete. Decision impact: prototype shared history, but delay full seat packaging.

Intent: credit balance and failed task dispute. Urgency: high because refund is requested today. Likely owner: billing support with task log from product operations. First reply: acknowledge the failed task, ask for task ID, and confirm the team will check credit usage. Missing details: account email, task ID, timestamp, and payment reference.

Action item: check the upload bug. Owner: Alex. Due date: not stated. Dependency: pricing copy needs legal review. Unresolved decision: launch timing is not finalized. Follow-up: schedule a decision point after legal review.

Ready: implementation and QA are complete. Blocked: docs and support macro are missing. Owner gap: no named support owner. Customer-facing copy: update docs before external announcement. Rollback note: feature flag rollback is acceptable if flag owner is named.

Untranslated term: AI-powered workflow and unlock feel pasted from English. Stiff phrase: creative output is vague for a product page. Missing context: what task becomes faster is not stated. Claim risk: faster needs evidence or narrower wording. Suggested rewrite: 用 Rivya 把提示词、模型选择和生成结果放在同一个工作流里，更快完成首版素材。

Summary: video export freezes after upload and may affect visible credits. Steps to reproduce: upload video, start export, observe freeze after processing begins. Expected behavior: export completes or returns a clear failure. Actual behavior: page freezes twice according to user report. Missing data: task ID, timestamp, file size, account email, credit balance before and after.

Signal: users may need clearer model selection help. Source: sales calls and rising model article traffic. Confidence: medium because sales notes are qualitative and traffic intent is broad. Why it matters: model confusion can slow first task completion. Next evidence: tag support tickets and prompt searches related to model choice.

Fit: Vendor A fits analytics needs better; Vendor B fits budget pressure. Risk: both need security review before purchase. Cost concern: Vendor A annual minimum may exceed current usage. Integration effort: Vendor B creates manual CSV work. Questions before purchase: security status, data export limits, and minimum term flexibility.

Theme: model choice clarity. Sample quote: users mention confusing model names. Frequency hint: three notes, likely worth validating. Product implication: add plain-language model guidance near the run panel. Follow-up question: does guidance improve first successful generation for new users. Edge case: team history request may belong to agency workflow research.

Unanswered objection: what happens when a generation fails. Unclear plan fit: team use is not explained. Evidence gap: fast creation needs a concrete path or example. Copy risk: no hidden fees is broad unless billing rules are visible. Suggested clarification: add credit refund behavior, team limits, and a short example workflow.

Proposed bet: template pages support growth and model example depth. Constraint: engineering capacity is competing with sign-in cleanup. Dependency: billing docs may be needed before pricing experiments. Decision needed: choose one growth bet and one reliability bet. Risk if deferred: support burden grows if billing docs stay unclear.

Customer goal: manage image workflows for a team. Follow-up outline: recap workflow fit, answer history behavior, and confirm team billing constraints. Next question: how many creators need access in the first month. Internal risk: team billing may not match current packaging. CRM update: interested agency; team billing is decision blocker.

Confirmed rule: failed generation credits can be reviewed when logs show provider failure. Customer answer: share that the team can review the task if the customer provides the task ID. What not to promise: automatic refund before log review. Escalation need: billing owner if logs confirm provider failure. Internal note: capture task ID and timestamp.

Revised reply: I cannot approve a refund from this message alone, but I can help review the failed task. Please send the task ID and the time it ran so we can check the logs. Tone change: firm but helpful. Removed risk: no unsupported blanket policy claim. Remaining caveat: refund depends on task review.

Revised macro: Thanks for sending this in. Please share the task ID and approximate time of the failed generation so we can review the logs. Required placeholders: task ID, task time, account email if needed. Policy boundary: do not promise a credit adjustment before review. Agent note: use only when the customer reports a failed generation.

Policy rule: expired credits are not automatically extended. Customer impact: campaign delay may be real but was external to Rivya. Precedent risk: extending without criteria creates inconsistent treatment. Escalation path: ask billing owner whether documented provider failure exists. Reply stance: acknowledge the request and explain review limits.

Vague claim: strong growth needs a metric and comparison period. Missing evidence: product quality improved needs defect, retention, or task success data. Defensive tone: more headcount to accelerate sounds unsupported. Clear ask: specify the decision, amount of capacity, and expected outcome. Rewrite direction: pair each claim with one evidence point.

Empathy: acknowledge the missed deadline without accepting unverified fault. Facts: task failure needs ID and logs. Limit: cannot confirm cause or compensation from the message alone. Next action: request task ID and escalation contact. Internal note: prioritize because customer mentions client deadline.

Plain-English risk: usage limits can change after purchase. Business impact: forecasted volume may become unreliable. Question for counsel: what notice period and termination rights apply. Negotiation point: lock limits for the initial term. Do not decide: legal enforceability without counsel.

Audience clarity: ecommerce operator or creative team is not specified. Evidence gap: speed and quality need examples or comparison criteria. Thin claim: all-in-one workspace is broad without a concrete workflow example. Next step: define one product photo scenario and required evidence. Risk: article may become generic list content.

Policy fit: public figure endorsement for advertising is sensitive and likely restricted. Missing facts: whether consent or licensed material exists. User impact: campaign timeline may be affected. Escalation recommendation: route to policy owner before generation. Safe reply direction: explain that consent and usage rights must be confirmed.

Affected surfaces: prompt examples, model cards, blog covers, search and share images. Owner actions: approve assets, update source links, and run final checks. Customer messaging: no visible promise is needed unless the URL change alters access. Legal question: retention and deletion policy for old draft files. Open risk: draft links can remain in source by mistake.

Tightened claim: the team is prioritizing reliable multimodal workflows this cycle. Evidence needed: current template coverage, model pages, and the path from prompt to result. Tradeoff: final media review slows launch but protects credibility. Ask: finish template review and asset checks before final release. Tone note: avoid best-platform language without evidence.

Stakeholders: design team, procurement, legal. Use cases: design workflow and generated media review. Risks: credit packaging and storage policy clarity. Expansion path: start with design team pilot, then workspace governance. Next meeting goal: confirm pilot scope and legal storage questions.

Main claim: prompt templates improve model page trust. Evidence: reusable examples are visible near model guidance. Weak link: trust improvement is not measured yet. Counterpoint: too many thin templates can reduce quality signals. Supported decision: add templates only when the conversation example is specific and useful.

User goal: understand where prompt templates appear and how to run them. Prerequisites: published template, recommended model, and supported mode. Steps: open the prompt, review the example, run or copy it, then continue in Studio. Edge cases: unavailable model, draft template, or media still awaiting final approval. Related links: prompt library, model pages, and media checklist.

Unclear action: Proceed does not say what happens next. Overloaded helper: advanced orchestration is internal language. Missing outcome: user needs to know model selection affects output style and cost. Suggested label: Choose this model. Suggested helper: Use this model for balanced image quality and edit control.

Observation: prompt examples help discovery but may not complete the workflow. Neutral follow-up: what makes you move the prompt into another tool. Behavior question: when did that happen in your last project. Avoid: asking whether Studio is missing export features. Decision link: learn whether continuation, trust, or habit causes tool switching.

Responsibilities: compatibility definitions, admin read display, and seed support. Callers: prompt library, admin prompt page, and validation scripts. Data flow: versioned templates are the current source of truth; defaults are compatibility examples. Risky assumption: removing defaults may break labels in old scripts. Safe first change: add a usage audit before deleting compatibility exports.

Likely cause: template source exists without matching locale file. Reproduction: add slug, run build, hit prompt static params. Minimal fix: add en and zh locale files for the slug. Regression test: run prompts:check before build. Do not change: route generation unless locale files are valid.

Contract drift: schema expects image, video, audio, null, or empty compatibility. Caller impact: admin form depends on empty string handling. Validation gap: test null and empty string separately. Rollout note: normalize to null at read boundary. Risk: strict parser may reject existing draft records.

Data loss risk: low if reads no longer depend on the old table. Backfill order: confirm release-file coverage before disabling writes. Rollback limit: re-enabling database writes may not recreate missed edits. Verification query: count published file templates against old rows. Decision: keep old rows read-only until one release passes.

Ticket 1: audit current asset paths and temporary link references. Ticket 2: replace final URLs and verify public pages. Ticket 3: remove the compatibility path only after example coverage holds. Ticket 4: update governance docs and release checklist. Verification: prompt checks, media audit, typecheck, and build.

Symptom: URL changes but textarea state does not refresh. Likely stale state: query hydration runs only on first mount. Reproduction: click two prompt cards on the same model page. Minimal fix: observe search params and sync only when relevant values change. Test: direct load and same-page navigation both refill textarea.

Redirect loop risk: sign-in should not redirect to itself. Locale handling: preserve zh in return path. Protected route leak: studio content remains noindex and gated. Test case: unauthenticated localized studio request. Regression check: default locale and zh should behave consistently.

Source of truth: result_urls_json first item for old completed tasks. Dry run: count missing primary URL by status. Write order: old completed tasks only, batch by ID. Verification: compare counts before and after. Rollback limit: primary URL can be cleared only if original JSON remains intact.

Missing log: provider error code and request ID. Missing metric: failure rate by provider and model. Missing trace: upload to generation handoff. Alert gap: no provider-specific spike alert. Next step: persist normalized upstream error source and code for support views.

API change: none confirmed yet. Generated files: no app files should change from browser install. Browser requirement: install matching Chromium revision. Fallback plan: use existing cached revision only if version matches. Verification: run the screenshot command after install and record revision.

Switch point: before deploy, rollback is git revert; after deploy, redeploy previous build. Data risk: none from schema, but sitemap count changes. Owner: release engineer for deploy; content owner for template validation. Verification: prompts:check, i18n checks, typecheck, build. Abort condition: missing locale file or prompt static route failure.

Likely cause: client filtering and card rendering scale with item count. Measurement plan: compare hydration time and filter input latency before and after. Safe experiment: memoize search values or virtualize only if needed. Rollback trigger: interaction latency exceeds target on mid-range mobile. Do not change: SEO static generation without evidence of server bottleneck.

Focus order: card link should not trap action buttons. Target size: copy and run buttons need at least 24px target or spacing. Reduced motion: hover sweep should be decorative only. Label check: buttons need visible or accessible action names. Mobile risk: conversation bubble text must not overlap actions.

User path: model detail to related prompt to run panel. Data boundary: prompt text travels through client navigation. Failure mode: textarea keeps stale prompt. Test case: click two different prompt cards and assert latest value. Verification target: URL and textarea stay in sync.

Idempotency: event ID must be unique before credit write. Replay safety: verify signature and timestamp tolerance. Credit write: ledger entry should reference checkout session. Customer-visible failure: show pending review if payment succeeded but credit write failed. Test gap: duplicate event and out-of-order event cases.

Order: audit current config, compare generated facts, then update seed script. Contract: model slug, category, and provider ID must stay stable. Verification: parity check before runtime change. Rollback boundary: config generation can revert independently from UI content. Risk: changing display fields may affect SEO pages.

Touched files: prompt template source and locale files. Invariants: no code path or route behavior changed. Known risk: new pages increase static prompt count. Verification: prompts:check and SEO title audit. Next owner decision: whether to run full build before merge.

Auth question: who can access reused prompts and uploaded references. Storage question: where reference assets live and when they expire. User data question: whether prompts can contain private customer data. Abuse path: public sharing could expose private media. Review owner: security and product need retention rules before launch.

Changed-file failure: inspect locale JSON shape first. Environment failure: not likely if prompts:check passed. Flaky test: unlikely for deterministic i18n:check. Next command: run i18n:generate, then i18n:check again. Do not do: revert generated files without understanding the source mismatch.

Context: public prompt pages need static, reviewable content. Options: database CMS, file source, or hybrid writeback. Decision: file source with admin diagnostics only. Consequences: deploy needed for edits, but SEO and review stay stable. Revisit trigger: operations need safe non-developer write workflow.

Target pattern: explicit field access in prompt source and view models. Exclusions: generated files and unrelated locale content. Review sampling: one template, one admin page, one public detail page. Formatting: run scoped formatter after codemod. Rollback: commit codemod separately from manual copy edits.

Producer: reviewed template records. Consumer: example aggregation and public cards. Compatibility window: older catalog rows remain read-only inventory. Validation: coverage checks and page sampling. Cleanup step: remove the old path only after final storage and page sampling pass.

Impact: new prompt pages are unavailable after deploy. Suspected scope: static route generation or missing content records. Safe patch: confirm templates are included in the release and rebuild. Verification: request one new English and Chinese prompt URL. Communication: content was added but pages need a rebuild; no user data is affected.

What it proves: prompt shape and required locale fields. Stale field: database ID no longer proves runtime behavior. Shared helper: build fixture from template slug and locale content. Safe deletion order: replace one fixture family, run prompts tests, then remove old IDs. Risk: admin compatibility tests may still need legacy ID examples.

Highest priority: old example compatibility path because it affects launch example credibility. Failure risk: duplicate prompt scripts can reintroduce stale write paths. Migration pressure: final storage migration blocks release confidence. Verification cost: browser sampling is manual but bounded. Recommendation: finish storage cleanup and compatibility removal before cosmetic cleanup.

Positioning: the page sells lower perceived risk before it sells features. Packaging: tier names are simple, but credit limits are under-explained. Objection handling: annual savings are visible; usage anxiety is deferred to the FAQ. Trust signals: logos near checkout support the final decision moment. Tests: move credit math into the tier cards and add one buyer-specific proof point per tier.

Missing intents: credit estimation before running a job and privacy boundaries for uploaded files. Prerequisites: setup should state required account role and billing state. Stale risk: export docs need screenshots for both image and video jobs. New articles: credit planning, private file lifecycle, and team role troubleshooting. Priority: write credit planning first because it reduces pre-purchase anxiety.

Likely cause: the first decision is too wide and appears risky. Evidence to collect: model dropdown opens, hover time, failed first-run events, and search terms. Copy fix: label one default as best for product visuals and one as best for edits. Product fix: preselect a safe default and hide advanced models behind comparison. One-week experiment: default to the highest-success image model and measure first job completion.

Confirmed: role-based access and audit logs are available for workspace administration. Planned: customer-managed keys are on the roadmap but should not be represented as currently available. Exception: encryption details should be answered by the security owner before submission. Suggested answer: state current controls, describe the CMK roadmap carefully, and offer a security follow-up. Follow-up: ask whether CMK is mandatory for pilot approval or only for production rollout.

Risk: notice may be easy to miss and may not give the team enough time to object. Business impact: privacy, procurement, and customer commitments could be affected. Question 1: can changes trigger email notice to the account owner? Question 2: is there an objection window for material subprocessor changes? Question 3: can regulated customer data be excluded from new subprocessors by default?

Theme 1: decision overload around model choice. Theme 2: cost anxiety around credits and usage predictability. Theme 3: trust and governance concerns around privacy and approvals. Recommended reply: lead with defaults, credit estimator, and workspace controls. Product follow-up: improve model recommendation, expose export actions, and document approval flows.

Progress: template coverage reached the current target and proof depth improved. Risk: media storage cleanup remains open before launch. Decision needed: whether page title reviews should be fixed before launch or tracked as P2. Customer signal: usage confusion is still creating support volume. Next focus: storage validation, usage estimator copy, and targeted title cleanup.

Intent alignment: both locales should lead with creating usable AI video, not only browsing models. English copy: keep cinematic clips, but add prompt templates and export workflow. Chinese copy: keep template speed, but add quality and controllable camera motion. Metadata: title should mention AI video generator and prompt workflow without keyword stuffing. Proof examples: use one product clip, one travel clip, and one avatar or talking-head workflow.

Touched surfaces: content file loader, example aggregation, detail pages, and modality pages. Hidden coupling: old inline samples may still affect compatibility visuals and sitemap images. Tests: prompt template check, model content audit, route render samples, and media audit. Rollout note: treat final asset storage as a separate release gate. Watch item: any page that assumes old inline samples are the primary proof source.

P0: prompt template schema and model category validation. P0: route rendering for one prompt page per mode. P1: SEO title and description length audit. P1: media URL existence for image, video, and audio prompts. P2: visual density checks for prompt listing filters after the count increase.

What changed: mediaUrl is now explicit by media type. Why it matters: clients can render the correct player or image component without guessing. Migration: map image assets to imageUrl, video files to videoUrl, audio files to audioUrl, and thumbnails to posterUrl. Compatibility: keep accepting mediaUrl during migration but log usage. Risk: ambiguous old values may produce incorrect previews if not mapped.

Behavior changes: module loading and config validation may fail earlier. Migration work: pin loader options, update invalid config, and refresh browser caches. Tests: run typecheck, build, and at least one browser screenshot flow. Rollback signals: unexplained startup failures, config parse errors, or missing browser executable errors. Owner: platform tooling should own the upgrade and cache note.

Timeline: deploy at 09:12, failures began at 09:18, rollback at 09:24, recovery at 09:31. Suspected trigger: media route change in the deploy. Customer impact: prompt detail pages could not load media previews for roughly 13 minutes. Mitigation: rollback restored traffic; keep the deploy frozen until route tests pass. Open questions: why prelaunch checks missed the route and whether cached pages masked the issue.

Entry points: content records, locale files, and shared rendering code. Core flow: template JSON plus locale JSON becomes public page content. Owned areas: content governance, media URL fields, and validation scripts. Risky areas: asset storage conventions, old sample data, and localized SEO metadata. First tasks: add one template, run content checks, inspect one page, then read the validation script.

Supported if measured: video prompt conversion can be stated only if analytics compare prompt and model pages. Weak claim: creators prefer one workspace needs survey or behavioral evidence. Missing evidence: audio template usage needs traffic, click, and completion data by mode. Safer wording: early signals suggest workflow pages may reduce decision friction. Next evidence: compare mode-level CTR, first-run completion, and repeat usage.

User value: onboarding templates and the usage estimator improve activation; asset storage cleanup improves reliability. Effort: template expansion is medium, storage cleanup is high, title refresh is low, estimator is medium-high. Dependencies: admin dashboard depends on clear asset object conventions. Tradeoff: more templates create more review debt if storage stays unresolved. Recommended scope: finish the template milestone, freeze new assets, run storage validation, then ship estimator copy.