writingcareer

Resume Bullet Point Optimizer

Transforms weak resume bullet points into impact-driven statements using the XYZ formula with quantified results.

Prompt

You are a professional resume writer and career coach who has helped 500+ clients in the [industry] sector land roles at top companies. I'll provide my current resume bullet points for a [job title] role. The goal is to create compelling, quantified achievements that pass ATS screening. Analyse, rewrite, and improve each bullet point using the Google XYZ formula: 'Accomplished [X] as measured by [Y], by doing [Z]'. For each bullet, format the output as a structured comparison: (1) original text, (2) rewritten version with a strong action verb, a quantified result, and the method used, (3) if no metric is provided, suggest a realistic metric and mark it with [estimate], (4) rate the original 1-5 and the rewritten version 1-5 with a one-line justification. You must start each bullet with a past-tense action verb. Avoid overused verbs such as 'Led', 'Managed', or 'Responsible for'. Only use specific verbs like 'Architected', 'Streamlined', or 'Negotiated'. Do not exceed 2 printed lines per bullet. For example, transform 'Managed a team' into 'Directed a cross-functional team of 8 engineers, delivering the v2.0 platform 3 weeks ahead of schedule'. After rewriting all bullets, provide a 'Top 5 Power Verbs for [industry]' list. Here are my current bullets:\n\n[PASTE BULLETS HERE]

Why this prompt works

The Google XYZ formula ('Accomplished [X] as measured by [Y], by doing [Z]') is the structural anchor of this prompt, and it's what stops the model from producing rewrites that sound impressive but lack measurable impact. The verb specificity rule ('avoid Led, Managed, Responsible for') with the example transformation ('Managed a team' becomes 'Directed a cross-functional team of 8 engineers, delivering the v2.0 platform 3 weeks ahead of schedule') gives the model a concrete pattern. The original-and-rewrite comparison format with the 1 to 5 rating per version is also useful: it shows the writer where the lift came from, which makes future bullets easier to write themselves.

When to reach for it

You're job-hunting and have a CV that hasn't been touched in 2+ years; this is a structured rewrite pass.
You're applying to a stretch role and want to reframe existing achievements with sharper impact language.
You're a career coach helping clients and want consistent rewrites you can quickly review and adjust.
You're updating LinkedIn rather than a CV; the same XYZ formula works there with minor formatting changes.

How to customise it

The job title and industry inputs shape the verb suggestions. 'Senior product manager in fintech' produces different power verbs than 'engineering manager in healthcare'. Be specific. For roles with quantified output (sales, marketing), the 'estimate' marker is genuinely useful; you can use the model's suggested numbers as anchors during interview prep, even if you don't put them on the CV. For roles where impact is harder to quantify (research, design, support), tell the model to favour 'method-driven' rewrites that emphasise the approach rather than the metric.

What good output looks like

A two-column-ish format with original on top, rewritten below, the action verb highlighted, the metric called out, and the method noted. Each bullet is rated original-X / rewritten-Y. Bullets that already met the bar get marked as such rather than re-written for the sake of it. After the bullets, a top-5 power verbs list specific to the industry. Word count varies based on input length.

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