🎬 Entertainment / Streaming

AI Review Assistant — Film & Series Feedback

Genre-aware feedback tags and voice-matched review drafts for any film or series. Workers AI makes every review specific and helpful.

The Problem

"Viewers want to share opinions but don't know how to articulate them. A blank box gets "great show" — which tells the next viewer nothing useful."

The Outcome

8

contextual feedback tags generated per film — specific to genre, tone, and themes. One click starts a meaningful review.

Live demo below

The problem

Only 3% of customers ever write a review. The rest abandon the blank form.

"We've A/B tested everything — stars, prompts, incentives. Nothing works. Customers open the review form, stare at the blank box, and close it."

— Head of E-commerce, 2M product reviews per year opportunity

Both sides run simultaneously — real API calls, no mocks

Without Cloudflare
Blank review form
🦇

The Dark Knight

2008 · ★ 9

Your rating:

★★★★★

Customer clicked stars — then stared at the blank box and left.

97% close this without typing anything.

Response time

—
With Cloudflare Workers AI
AI-powered review assistant

1. Rate it — AI adapts tone to your stars:

Loved it

Tone: enthusiastic · very positive — loved it

🦇

The Dark Knight

2008 · ★ 9

5★ · enthusiastic

Click "Run Demo" to generate AI review suggestions

Response time

—

The win

⭐
5 tones
Star-driven tone
5★=enthusiastic · 3★=balanced · 1★=critical
📈
3×
Review completion
customers with AI starting points convert better
⚡
<1s
Tag generation
Llama 3.1 at the Cloudflare edge

Productionising this

What changes when you ship this for real

Tag taxonomy

AI-generated pills can drift. Maintain a curated tag taxonomy per category (sofa, bed, etc.); have the LLM pick from this list rather than free-generate. Better consistency for analytics.

Server-side moderation

Before persisting any AI-drafted review, run @cf/openai/* moderation or LlamaGuard on the output. Don't let a confused LLM publish something inappropriate under the user's name.

Edit, don't replace

Default the form to "AI suggested — edit before posting". Track edit-rate as a quality signal: if users edit >50%, your prompts need work.

Cost per draft

glm-4.7-flash: $0.06 per 1M input + $0.40 per 1M output tokens. A 200-token review draft ≈ $0.00005 — essentially free at SE-demo scale.

Cache by item id

Pills for the same product/film are stable. KV-cache the pill list per item with 1h TTL. Saves neurons + improves perceived responsiveness.

Fallback path

On AI.run failure, return the pre-curated pill list (already in the demo as the catch-fallback). Demo never breaks even if Workers AI has a hiccup.