How to Build a Cover Letter Generator Prompt That Never Invents Experience
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When you ask a standard chat model to tailor your application to a job description, it often fills gaps by inventing responsibilities, unverified metrics, or phantom company initiatives. Explaining an AI-invented statistic in an interview ruins your credibility immediately.
To keep an LLM from inventing achievements, treat it like a compiler that works only from verified raw inputs.
The Core Failure of Standard Tailoring Prompts
Standard prompts fail because they ask for creative synthesis without firm boundaries. When a job description asks for five years of Kubernetes experience and your pasted notes mention only Docker, a broad prompt often instructs the model to make you look like an ideal fit. The model complies by inflating your background.
A grounded cover letter generator prompt treats your career history as a closed database. If a specific tool, team size, or dollar figure is not in your source text, the prompt must explicitly bar the model from inventing or deducing it.
How to Fence the Model with Source Constraints
Grounded prompting relies on three rules passed directly in the chat interface:
The Drop-In Grounded Cover Letter Prompt
Paste this structure into Claude, ChatGPT, or a local model, replacing the bracketed inputs with your raw text.
You are a professional application drafting assistant. Your task is to draft a three-paragraph cover letter using ONLY the facts present in <source_history> to address the needs in <job_description>.
Strict Rules:
1. Do not invent metrics, tools, project names, or timeframes.
2. If the job description requires a skill not found in <source_history>, do not mention it in the cover letter.
3. Keep tone direct, professional, and concise. Avoid corporate clichés like 'thrilled to apply' or 'dynamic synergy'.
4. Output a final section titled [Omitted Requirements] listing any key role requirement that had no matching evidence in my history.
<job_description>
[Paste full job posting here]
</job_description>
<source_history>
[Paste your verified career history or resume bullets here]
</source_history>Reviewing the Output for Leakage
Check the draft before saving it. Read every sentence containing an action verb or a number, and compare it against your master resume.
If the model wrote "collaborated cross-functionally with senior leadership," check whether your notes said that or if the model added corporate filler. Delete any sentence that fails a factual check against your source.
FAQ
Why did the model still invent a percentage even with rules?
Some base models prioritize narrative flow over strict instructions. If this happens, set the temperature to 0.1 or tell the model: "You included a statistic not found in my notes; remove it immediately."
Can I feed an entire 10-page portfolio into the source history?
Yes, if the model has enough context window. Keeping the source history to 400 to 800 words of relevant bullets produces tighter cover letters.
Does this structure bypass ATS filters?
Applicant Tracking Systems index keywords and formatting rather than checking whether an AI drafted the text. Grounding keeps the keywords in your letter tied directly to your actual experience.