Chat Prompt Templates for Strategy Briefs and Research

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.