Ask a general-purpose chatbot to write your resume and it will produce something fluent, confident and partly false. It will round your team size up, attach a percentage to an outcome you never measured, and quietly promote you. That is not a flaw in the prompt — it is what a language model does when it has no facts to work from.
This builder solves the problem at the source. Before any AI runs, you fill in a structured career profile. Every generation is then constrained to that profile by a hard grounding contract, and the model is instructed to emit a visible placeholder rather than fabricate a missing number.
The grounding contract, in plain terms
Every AI call in this product carries the same non-negotiable instruction set. The model may rephrase, compress, expand, reorder and prioritise facts from your profile. It may not introduce employers, job titles, dates, companies, degrees, institutions, certifications, skills, tools, responsibilities, achievements, metrics or results that are not already there.
When a sentence would be stronger with a number you have not supplied, the output contains [ADD METRIC] — a marker you can see and fill in — instead of a plausible-sounding invention. You are never handed a lie you have to spot.
- No invented employers, titles, dates or degrees
- No invented metrics, percentages, team sizes or revenue figures
- No claimed proficiency in a skill you did not list
- Visible [ADD METRIC] placeholders instead of fabrication
- Gaps are never papered over with implied experience
Six writers, each doing one job properly
- Bullet generator — turns a responsibility into two or three achievement-shaped bullets, using only the outcomes you recorded
- Professional summary — a 40–70 word opener aimed at a specific target role
- Skills generator — organises what you listed into technical, tools, domain and soft groups, and flags skills implied by your bullets but missing from your list
- Bullet rewriter — takes one weak line and returns stronger alternatives, preserving every fact
- Tailored resume — reprioritises your existing content for one job description
- LinkedIn headline and About — the same facts, rewritten for a first-person profile
Why grounding produces better writing, not just safer writing
Constraint is why the output reads like a person. A model with no facts reaches for adjectives — 'results-driven', 'dynamic', 'proven track record' — because adjectives are free. A model given a real project, a real tool and a real outcome writes a specific sentence, because specificity is available.
The grounding contract also bans that filler vocabulary outright, along with corporate cliché and adjective stacking. What comes back is short, active and concrete: verb, object, scope, result.
You stay in control of every word
Nothing the AI writes is applied automatically. Each generation appears as a suggestion you can accept, edit or discard, and every accepted change is undoable. The AI drafts; you sign.
Frequently asked questions
- How is this different from asking ChatGPT to write my resume?
- A general chatbot has no record of your career, so it fills the gaps with invention. Here, the AI is given your structured profile and a contract that forbids adding anything not in it. It also knows the resume conventions and ATS constraints the general model does not apply by default.
- Can the AI still get things wrong?
- It can phrase something awkwardly, or emphasise the wrong achievement for a particular job. What it will not do is invent a fact. Read everything before you send it — but you are proofreading, not fact-checking.
- Do I need my own OpenAI API key?
- No. AI is built in. There is nothing to configure and no key to supply.
- How many AI generations do I get?
- Ten a month on the Free plan. Unlimited on Pro and Lifetime.
- Will a recruiter be able to tell my resume was written with AI?
- Not if the facts are yours, which here they always are. What recruiters actually notice is generic phrasing — the 'results-driven professional with a proven track record' register. The grounding contract bans that language, so the output reads more specific than most human-written resumes, not less.