What this prompt does
This prompt audits and optimizes a developer-facing landing page for conversions. You supply [product_name], [product_type], [audience], and your [current_cr] and [target_cr], and the AI works through ten levers: [headline_variants] headline options, an objection-addressing sub-headline, a social-proof strategy, technical credibility signals, [cta_count] CTA variants, above-the-fold hierarchy, benefit-led feature presentation with code examples, pricing layout and anchoring, trust elements, and page-speed optimization for [tech_stack] under a [speed_target] LCP. It returns wireframe descriptions and copy per section.
The structure works because developers convert on evidence, not hype. The prompt forces the model toward the signals that actually move a technical audience — code samples, benchmark results, architecture clarity, and a fast above-the-fold — rather than generic marketing copy. A developer scanning a landing page is asking "does this actually work and is it worth my time," so the page has to answer with proof, not adjectives. By framing it as an audit against a [current_cr] baseline with a [target_cr] goal, it keeps every recommendation tied to conversion rather than aesthetics, and the page-speed requirement under [speed_target] LCP acknowledges that a slow hero loses the visitor before any copy is even read.
When to use it
- You have a developer-targeted product page that underconverts and need a structured audit.
- You want
[headline_variants]to A/B test different value propositions. - You need technical credibility signals — code samples, benchmarks, architecture — placed well.
- You are optimizing CTA copy and placement with
[cta_count]variants. - You want a pricing section with deliberate anchoring rather than a flat table.
- Your page is slow and you need
[tech_stack]-specific speed wins under[speed_target]LCP.
Example output
Expect a section-by-section audit with wireframe descriptions and ready-to-use copy: [headline_variants] headlines tied to distinct value props, a sub-headline answering the top objection, a social-proof and technical-credibility plan, [cta_count] CTA variants, an above-the-fold layout, a pricing-anchoring approach, and concrete speed optimizations for [tech_stack].
Pro tips
- Make each of the
[headline_variants]test a genuinely different value proposition, not reworded versions of the same claim — that is what makes the A/B test informative. - Feed the model an accurate
[audience]; "remote engineering teams of 5-50" produces sharper copy and objections than a vague "developers." - Push hardest on technical credibility — real code samples and benchmark numbers earn a developer's click far more than testimonials alone.
- Treat the above-the-fold and
[speed_target]LCP as linked: a slow hero kills conversions before any copy is read. - Use a realistic
[current_cr]baseline so the[target_cr]gap is plausible; an unrealistic jump skews the model toward overpromising tactics. - Lead with benefits framed in the developer's terms, then back each with a code example — the prompt's benefit-over-features instruction only works if the proof sits right beside the claim.
- After the first pass, ask the model to prioritize the ten levers by likely conversion impact so you know what to test first.
