Prompt in Claude Before Costly AI Ad Generation
Refine detailed prompts in cheap text models like Claude—researching product benefits, positioning, and platform best practices—before using Replet 4's ad skill to avoid burning credits on poor first drafts.
Craft Prompts That Research and Position Like an Expert
To generate effective ads for LinkedIn, Instagram, and Google, start by prompting a strong text model like Claude to build a master prompt. Feed it your product (e.g., HubSpot's Breeze customer agent, which resolves 65% of tickets automatically, sets up in minutes, works across chat/email/WhatsApp/voice, needs no code). Instruct Claude to research core benefits (77% fewer tickets, zero new hires for some customers, 39% faster resolution), competitive positioning, brand voice (HubSpot's sprocket logo, not a steering wheel), and platform-specific best practices. The output is a massive, structured prompt positioning you as an "elite performance creative strategist managing $50M in B2B SaaS ad spend." It specifies ad types (e.g., LinkedIn carousel/image/video, Instagram stories/reels, Google responsive search ads), angles (pain points like too many tickets/too few staff, proof points), and outputs three ads per platform. This zero-to-one step baselines even non-experts, saving credits since text iteration costs far less than visual generation—e.g., $20/month Replet plan burns fast on bad prompts.
Iterate this prompt manually: Edit sections for accuracy, add a "not-do" list (avoid post-apocalyptic illustrations, non-brand colors like weird blues, generic images). Result: Ads with data-driven hooks ("77% fewer tickets"), teammate framing ("Not a chatbot, your AI support teammate"), and intent-matched copy ("Too many tickets? 65% auto-resolved").
Generate and Visualize Ads in Replet 4's Canvas
Paste the refined prompt into Replet 4's new "Ad Creative" skill for platform-tailored outputs. Replet, a vibe-coding tool, translates natural language to code generating ads, now with a canvas for GUI edits (drag, spot-fix components). It produces:
- LinkedIn: Carousel/image ads with customer results (e.g., Neutrabees: 77% fewer tickets), whiteboard styles, before/afters—but often flawed visuals (illegible text overlays, wrong logos, commercial fades).
- Instagram: Scroll-stopping reels/stories with Instagrammy before/afters, data proofs—but risky illustrations or off-brand blues.
- Google Responsive Search: Strongest output—visualizes search previews with scored headlines (e.g., "Winning: Too many tickets, too few staff—65% auto-resolved"), multiple variants ("Set up in minutes," "39% faster resolution"), CTAs ("Start for free"). No heavy visuals needed, so copy shines.
Replet scores elements (e.g., headline grades) and enables in-canvas iteration: Select an ad/component, prompt revs like "Redo with real HubSpot logo, better image, legible text." Provide samples (10-20 logo/image versions) for faster wins.
Expect 2+ Hours of Iteration for Production-Ready Ads
AI excels at copywriting and baselines (e.g., intent-matching Google headlines convert well) but falters on visuals—state-of-the-art tools like Replet 4, Super Scale still produce terrible graphics (overlaps, irrelevance, generic AI art). First gens often fail: 1/3 LinkedIn ads unusable, Instagram hit-or-miss. Iterating visuals costs $20-40 in credits; pair with Canva for cheap polishes if design-skilled.
Trade-offs: Great for non-designers testing $100 ad budgets; slower than manual Canva for pros. Not one-shot—expect hours for 9 solid ads (3/platform), improving via loop marketing (express-tailor-amplify-evolve: learn from tests, refine next batch). Supply existing creatives/brand assets upfront for better first revs. Tools like Replet 4 reduce friction but demand prompt discipline to hit pro standards worth running.